Building Vocabulary from Tokenized Words in The Iliad Dataset Using TensorFlow

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💡 Problem Formulation: When working with natural language processing, creating a vocabulary from a tokenized text is crucial. The goal is to convert the Iliad dataset, which has been tokenized into words, into a consistent vocabulary that a machine learning model can understand. We aim to structure this vocabulary for efficient training and inference using TensorFlow in Python.

Method 1: Using TensorFlow and Keras Preprocessing

The TensorFlow Keras library provides an efficient method for creating a vocabulary from tokenized words through the Tokenizer class. This method allows the collection of word frequency, filtering of uncommon words, and conversion to sequences that the model can process.

Here’s an example:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.preprocessing.text import Tokenizer

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Creating and fitting the tokenizer
tokenizer = Tokenizer()
tokenizer.fit_on_texts(tokenized_text)

# Vocabulary mapping
word_index = tokenizer.word_index
print(word_index)

Output of the code snippet:

{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.preprocessing.text import Tokenizer

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Creating and fitting the tokenizer
tokenizer = Tokenizer()
tokenizer.fit_on_texts(tokenized_text)

# Vocabulary mapping
word_index = tokenizer.word_index
print(word_index)

Output of the code snippet:

{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.preprocessing.text import Tokenizer

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Creating and fitting the tokenizer
tokenizer = Tokenizer()
tokenizer.fit_on_texts(tokenized_text)

# Vocabulary mapping
word_index = tokenizer.word_index
print(word_index)

Output of the code snippet:

{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.preprocessing.text import Tokenizer

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Creating and fitting the tokenizer
tokenizer = Tokenizer()
tokenizer.fit_on_texts(tokenized_text)

# Vocabulary mapping
word_index = tokenizer.word_index
print(word_index)

Output of the code snippet:

{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.preprocessing.text import Tokenizer

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Creating and fitting the tokenizer
tokenizer = Tokenizer()
tokenizer.fit_on_texts(tokenized_text)

# Vocabulary mapping
word_index = tokenizer.word_index
print(word_index)

Output of the code snippet:

{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.preprocessing.text import Tokenizer

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Creating and fitting the tokenizer
tokenizer = Tokenizer()
tokenizer.fit_on_texts(tokenized_text)

# Vocabulary mapping
word_index = tokenizer.word_index
print(word_index)

Output of the code snippet:

{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.preprocessing.text import Tokenizer

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Creating and fitting the tokenizer
tokenizer = Tokenizer()
tokenizer.fit_on_texts(tokenized_text)

# Vocabulary mapping
word_index = tokenizer.word_index
print(word_index)

Output of the code snippet:

{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.preprocessing.text import Tokenizer

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Creating and fitting the tokenizer
tokenizer = Tokenizer()
tokenizer.fit_on_texts(tokenized_text)

# Vocabulary mapping
word_index = tokenizer.word_index
print(word_index)

Output of the code snippet:

{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.preprocessing.text import Tokenizer

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Creating and fitting the tokenizer
tokenizer = Tokenizer()
tokenizer.fit_on_texts(tokenized_text)

# Vocabulary mapping
word_index = tokenizer.word_index
print(word_index)

Output of the code snippet:

{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.preprocessing.text import Tokenizer

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Creating and fitting the tokenizer
tokenizer = Tokenizer()
tokenizer.fit_on_texts(tokenized_text)

# Vocabulary mapping
word_index = tokenizer.word_index
print(word_index)

Output of the code snippet:

{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.preprocessing.text import Tokenizer

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Creating and fitting the tokenizer
tokenizer = Tokenizer()
tokenizer.fit_on_texts(tokenized_text)

# Vocabulary mapping
word_index = tokenizer.word_index
print(word_index)

Output of the code snippet:

{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.preprocessing.text import Tokenizer

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Creating and fitting the tokenizer
tokenizer = Tokenizer()
tokenizer.fit_on_texts(tokenized_text)

# Vocabulary mapping
word_index = tokenizer.word_index
print(word_index)

Output of the code snippet:

{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.preprocessing.text import Tokenizer

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Creating and fitting the tokenizer
tokenizer = Tokenizer()
tokenizer.fit_on_texts(tokenized_text)

# Vocabulary mapping
word_index = tokenizer.word_index
print(word_index)

Output of the code snippet:

{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.preprocessing.text import Tokenizer

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Creating and fitting the tokenizer
tokenizer = Tokenizer()
tokenizer.fit_on_texts(tokenized_text)

# Vocabulary mapping
word_index = tokenizer.word_index
print(word_index)

Output of the code snippet:

{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.preprocessing.text import Tokenizer

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Creating and fitting the tokenizer
tokenizer = Tokenizer()
tokenizer.fit_on_texts(tokenized_text)

# Vocabulary mapping
word_index = tokenizer.word_index
print(word_index)

Output of the code snippet:

{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.preprocessing.text import Tokenizer

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Creating and fitting the tokenizer
tokenizer = Tokenizer()
tokenizer.fit_on_texts(tokenized_text)

# Vocabulary mapping
word_index = tokenizer.word_index
print(word_index)

Output of the code snippet:

{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.preprocessing.text import Tokenizer

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Creating and fitting the tokenizer
tokenizer = Tokenizer()
tokenizer.fit_on_texts(tokenized_text)

# Vocabulary mapping
word_index = tokenizer.word_index
print(word_index)

Output of the code snippet:

{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.preprocessing.text import Tokenizer

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Creating and fitting the tokenizer
tokenizer = Tokenizer()
tokenizer.fit_on_texts(tokenized_text)

# Vocabulary mapping
word_index = tokenizer.word_index
print(word_index)

Output of the code snippet:

{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.preprocessing.text import Tokenizer

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Creating and fitting the tokenizer
tokenizer = Tokenizer()
tokenizer.fit_on_texts(tokenized_text)

# Vocabulary mapping
word_index = tokenizer.word_index
print(word_index)

Output of the code snippet:

{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.preprocessing.text import Tokenizer

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Creating and fitting the tokenizer
tokenizer = Tokenizer()
tokenizer.fit_on_texts(tokenized_text)

# Vocabulary mapping
word_index = tokenizer.word_index
print(word_index)

Output of the code snippet:

{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.preprocessing.text import Tokenizer

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Creating and fitting the tokenizer
tokenizer = Tokenizer()
tokenizer.fit_on_texts(tokenized_text)

# Vocabulary mapping
word_index = tokenizer.word_index
print(word_index)

Output of the code snippet:

{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.preprocessing.text import Tokenizer

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Creating and fitting the tokenizer
tokenizer = Tokenizer()
tokenizer.fit_on_texts(tokenized_text)

# Vocabulary mapping
word_index = tokenizer.word_index
print(word_index)

Output of the code snippet:

{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.preprocessing.text import Tokenizer

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Creating and fitting the tokenizer
tokenizer = Tokenizer()
tokenizer.fit_on_texts(tokenized_text)

# Vocabulary mapping
word_index = tokenizer.word_index
print(word_index)

Output of the code snippet:

{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.preprocessing.text import Tokenizer

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Creating and fitting the tokenizer
tokenizer = Tokenizer()
tokenizer.fit_on_texts(tokenized_text)

# Vocabulary mapping
word_index = tokenizer.word_index
print(word_index)

Output of the code snippet:

{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.preprocessing.text import Tokenizer

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Creating and fitting the tokenizer
tokenizer = Tokenizer()
tokenizer.fit_on_texts(tokenized_text)

# Vocabulary mapping
word_index = tokenizer.word_index
print(word_index)

Output of the code snippet:

{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.preprocessing.text import Tokenizer

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Creating and fitting the tokenizer
tokenizer = Tokenizer()
tokenizer.fit_on_texts(tokenized_text)

# Vocabulary mapping
word_index = tokenizer.word_index
print(word_index)

Output of the code snippet:

{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.preprocessing.text import Tokenizer

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Creating and fitting the tokenizer
tokenizer = Tokenizer()
tokenizer.fit_on_texts(tokenized_text)

# Vocabulary mapping
word_index = tokenizer.word_index
print(word_index)

Output of the code snippet:

{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.preprocessing.text import Tokenizer

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Creating and fitting the tokenizer
tokenizer = Tokenizer()
tokenizer.fit_on_texts(tokenized_text)

# Vocabulary mapping
word_index = tokenizer.word_index
print(word_index)

Output of the code snippet:

{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.preprocessing.text import Tokenizer

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Creating and fitting the tokenizer
tokenizer = Tokenizer()
tokenizer.fit_on_texts(tokenized_text)

# Vocabulary mapping
word_index = tokenizer.word_index
print(word_index)

Output of the code snippet:

{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.preprocessing.text import Tokenizer

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Creating and fitting the tokenizer
tokenizer = Tokenizer()
tokenizer.fit_on_texts(tokenized_text)

# Vocabulary mapping
word_index = tokenizer.word_index
print(word_index)

Output of the code snippet:

{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.preprocessing.text import Tokenizer

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Creating and fitting the tokenizer
tokenizer = Tokenizer()
tokenizer.fit_on_texts(tokenized_text)

# Vocabulary mapping
word_index = tokenizer.word_index
print(word_index)

Output of the code snippet:

{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.preprocessing.text import Tokenizer

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Creating and fitting the tokenizer
tokenizer = Tokenizer()
tokenizer.fit_on_texts(tokenized_text)

# Vocabulary mapping
word_index = tokenizer.word_index
print(word_index)

Output of the code snippet:

{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.preprocessing.text import Tokenizer

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Creating and fitting the tokenizer
tokenizer = Tokenizer()
tokenizer.fit_on_texts(tokenized_text)

# Vocabulary mapping
word_index = tokenizer.word_index
print(word_index)

Output of the code snippet:

{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.preprocessing.text import Tokenizer

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Creating and fitting the tokenizer
tokenizer = Tokenizer()
tokenizer.fit_on_texts(tokenized_text)

# Vocabulary mapping
word_index = tokenizer.word_index
print(word_index)

Output of the code snippet:

{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.preprocessing.text import Tokenizer

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Creating and fitting the tokenizer
tokenizer = Tokenizer()
tokenizer.fit_on_texts(tokenized_text)

# Vocabulary mapping
word_index = tokenizer.word_index
print(word_index)

Output of the code snippet:

{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.preprocessing.text import Tokenizer

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Creating and fitting the tokenizer
tokenizer = Tokenizer()
tokenizer.fit_on_texts(tokenized_text)

# Vocabulary mapping
word_index = tokenizer.word_index
print(word_index)

Output of the code snippet:

{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.
from tensorflow.keras.preprocessing.text import Tokenizer

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Creating and fitting the tokenizer
tokenizer = Tokenizer()
tokenizer.fit_on_texts(tokenized_text)

# Vocabulary mapping
word_index = tokenizer.word_index
print(word_index)

Output of the code snippet:

{'sing': 1, 'o': 2, 'goddess': 3, 'the': 4, 'anger': 5, 'of': 6, 'achilles': 7}

This code snippet creates a Tokenizer instance and fits it to a sample from the Iliad dataset. The fit_on_texts method learns the word frequencies which are then used to create a dictionary of words mapped to unique integers.

Method 2: Utilizing TensorFlow’s StringLookup Layer

TensorFlow also offers the StringLookup layer, which can be used for vocabulary generation. It translates a set of strings to integer indices and is useful for building learning models directly within TensorFlow’s ecosystem.

Here’s an example:

from tensorflow.keras.layers.experimental.preprocessing import StringLookup

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create the StringLookup layer
string_lookup = StringLookup(vocabulary=list(vocab))

# Encode sample tokens
encoded_tokens = string_lookup(tokenized_text)

print(encoded_tokens.numpy())

Output of the code snippet:

array([7, 6, 2, 4, 1, 5, 3], dtype=int64)

The snippet uses the StringLookup layer to build a vocabulary from the unique tokens. By calling the layer with the tokenized text as an argument, it returns the encoded tokens as integers matching the learned vocabulary.

Method 3: Building a Vocabulary with TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets, including utilities for building vocabularies. The features.text.TokenTextEncoder class can be used for encoding text into integers suitable for model training.

Here’s an example:

import tensorflow_datasets as tfds

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
encoder = tfds.deprecated.text.TokenTextEncoder(tokenized_text)

# Encode sample sentence
encoded_sentence = encoder.encode("Sing O goddess the anger of Achilles")

print(encoded_sentence)

Output of the code snippet:

[1, 2, 3, 4, 5, 6, 7]

The TokenTextEncoder class provided by TensorFlow Datasets package is applied to the tokenized text, creating an encoder instance. Then, a sample sentence is encoded by calling the encode method and printing the list of integers.

Method 4: TensorFlow Text APIs for Vocabulary Construction

The TensorFlow Text library offers APIs that are especially optimized for text processing. It includes utilities for building vocabularies from tokenized text, which can be very handy for processing large datasets like the Iliad.

Here’s an example:

import tensorflow_text as text

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]
vocab = set(tokenized_text)

# Create a TextVectorization layer
vectorization_layer = text.TextVectorization(vocabulary=vocab)

# Vocabulary mapping
print(vectorization_layer.get_vocabulary())

Output of the code snippet:

['anger', 'Sing', 'O', 'goddess', 'the', 'of', 'Achilles']

In this code, a TextVectorization layer from TensorFlow Text is used to create a vocabulary. The layer is initialized with the unique words from the tokenized text, and the vocabulary can be retrieved with the get_vocabulary method.

Bonus One-Liner Method 5: Vocabulary with Python Collections

A quick solution for building a vocabulary from a list of tokenized words can also be achieved using Python’s built-in Collections library.

Here’s an example:

from collections import Counter

# Sample tokenized Iliad dataset
tokenized_text = ["Sing", "O", "goddess", "the", "anger", "of", "Achilles"]

# Building vocabulary with Counter
vocab = Counter(tokenized_text)

print(vocab)

Output of the code snippet:

Counter({'Sing': 1, 'O': 1, 'goddess': 1, 'the': 1, 'anger': 1, 'of': 1, 'Achilles': 1})

This one-liner utilizes Counter from Python’s Collections to build the vocabulary. The function creates a dictionary where the keys are the words and the values are their counts.

Summary/Discussion

  • Method 1: TensorFlow Keras Tokenizer. Strengths: Easy to use, integrates directly with Keras. Weaknesses: Limited customizability.
  • Method 2: TensorFlow’s StringLookup layer. Strengths: Direct integration with TensorFlow, good for large vocabularies. Weaknesses: Less flexibility compared to full-feature NLP libraries.
  • Method 3: TensorFlow Datasets’ TokenTextEncoder. Strengths: Designed for datasets available through TensorFlow Datasets. Weaknesses: Dependent on the tfds library which may not include all datasets.
  • Method 4: TensorFlow Text APIs. Strengths: Offers advanced text processing capabilities. Weaknesses: Adds an extra library dependency.
  • Bonus Method 5: Python Collections Counter. Strengths: Simple and requires no external libraries. Weaknesses: Basic functionality with no direct TensorFlow integration.