Menu iconMenu iconIntroduction to Natural Language Processing with Transformers
Introduction to Natural Language Processing with Transformers

Chapter 9: Implementing Transformer Models with Popular Libraries

9.2 Tokenization with Hugging Face’s Transformers Library

Tokenization plays a fundamental role in any Natural Language Processing (NLP) task as it is the first step towards processing unstructured text data. It involves the process of breaking down the input text into smaller units called tokens, which can be understood by the machine learning models.

The tokenization process is used to establish the boundaries of a word, sentence, or a paragraph. It is important to note that different transformer models may require different kinds of tokenization techniques, which can be based on word, sub-word, or character-level tokenization, among others. Therefore, it is crucial to choose the appropriate tokenization method depending on the task at hand to ensure that the language model is trained accurately and effectively.

Hugging Face's Transformers library provides tokenizers for all the models it supports. These tokenizers do all the necessary preprocessing for the models' input data, such as:

  • Splitting the input into words, subwords, or symbols (like BERT's WordPiece or GPT-2's Byte-Pair Encoding (BPE)).
  • Mapping those tokens to their corresponding input IDs, which are integer values. Each unique token has a unique ID.
  • Creating the attention masks, which are binary tensors specifying which positions in the input the model should pay attention to.

Example:

Here's an example of how to use a tokenizer in Hugging Face’s Transformers library:

from transformers import BertTokenizer

# Initialize the tokenizer using pre-trained BERT Base Uncased model
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

# The sentence to be tokenized
sentence = 'Hello, I am learning about Hugging Face Transformers.'

# Use the tokenizer
tokens = tokenizer(sentence)

print(tokens)

This will output a dictionary with the tokenized input:

{'input_ids': [101, 7592, 1010, 1045, 2572, 4083, 2055, 17662, 2227, 19081, 1012, 102], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}

In the output:

  • input_ids are the unique IDs for each token in the sentence.
  • attention_mask is a binary tensor indicating the position of the padded indices so that the model does not attend to them.

This section covered how to tokenize your text data using Hugging Face’s Transformers Library. But, tokenization is just the beginning. In the next sections, we will learn how to use Hugging Face's Transformers library for a wide variety of tasks such as text classification, named entity recognition, and question answering.

9.2 Tokenization with Hugging Face’s Transformers Library

Tokenization plays a fundamental role in any Natural Language Processing (NLP) task as it is the first step towards processing unstructured text data. It involves the process of breaking down the input text into smaller units called tokens, which can be understood by the machine learning models.

The tokenization process is used to establish the boundaries of a word, sentence, or a paragraph. It is important to note that different transformer models may require different kinds of tokenization techniques, which can be based on word, sub-word, or character-level tokenization, among others. Therefore, it is crucial to choose the appropriate tokenization method depending on the task at hand to ensure that the language model is trained accurately and effectively.

Hugging Face's Transformers library provides tokenizers for all the models it supports. These tokenizers do all the necessary preprocessing for the models' input data, such as:

  • Splitting the input into words, subwords, or symbols (like BERT's WordPiece or GPT-2's Byte-Pair Encoding (BPE)).
  • Mapping those tokens to their corresponding input IDs, which are integer values. Each unique token has a unique ID.
  • Creating the attention masks, which are binary tensors specifying which positions in the input the model should pay attention to.

Example:

Here's an example of how to use a tokenizer in Hugging Face’s Transformers library:

from transformers import BertTokenizer

# Initialize the tokenizer using pre-trained BERT Base Uncased model
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

# The sentence to be tokenized
sentence = 'Hello, I am learning about Hugging Face Transformers.'

# Use the tokenizer
tokens = tokenizer(sentence)

print(tokens)

This will output a dictionary with the tokenized input:

{'input_ids': [101, 7592, 1010, 1045, 2572, 4083, 2055, 17662, 2227, 19081, 1012, 102], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}

In the output:

  • input_ids are the unique IDs for each token in the sentence.
  • attention_mask is a binary tensor indicating the position of the padded indices so that the model does not attend to them.

This section covered how to tokenize your text data using Hugging Face’s Transformers Library. But, tokenization is just the beginning. In the next sections, we will learn how to use Hugging Face's Transformers library for a wide variety of tasks such as text classification, named entity recognition, and question answering.

9.2 Tokenization with Hugging Face’s Transformers Library

Tokenization plays a fundamental role in any Natural Language Processing (NLP) task as it is the first step towards processing unstructured text data. It involves the process of breaking down the input text into smaller units called tokens, which can be understood by the machine learning models.

The tokenization process is used to establish the boundaries of a word, sentence, or a paragraph. It is important to note that different transformer models may require different kinds of tokenization techniques, which can be based on word, sub-word, or character-level tokenization, among others. Therefore, it is crucial to choose the appropriate tokenization method depending on the task at hand to ensure that the language model is trained accurately and effectively.

Hugging Face's Transformers library provides tokenizers for all the models it supports. These tokenizers do all the necessary preprocessing for the models' input data, such as:

  • Splitting the input into words, subwords, or symbols (like BERT's WordPiece or GPT-2's Byte-Pair Encoding (BPE)).
  • Mapping those tokens to their corresponding input IDs, which are integer values. Each unique token has a unique ID.
  • Creating the attention masks, which are binary tensors specifying which positions in the input the model should pay attention to.

Example:

Here's an example of how to use a tokenizer in Hugging Face’s Transformers library:

from transformers import BertTokenizer

# Initialize the tokenizer using pre-trained BERT Base Uncased model
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

# The sentence to be tokenized
sentence = 'Hello, I am learning about Hugging Face Transformers.'

# Use the tokenizer
tokens = tokenizer(sentence)

print(tokens)

This will output a dictionary with the tokenized input:

{'input_ids': [101, 7592, 1010, 1045, 2572, 4083, 2055, 17662, 2227, 19081, 1012, 102], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}

In the output:

  • input_ids are the unique IDs for each token in the sentence.
  • attention_mask is a binary tensor indicating the position of the padded indices so that the model does not attend to them.

This section covered how to tokenize your text data using Hugging Face’s Transformers Library. But, tokenization is just the beginning. In the next sections, we will learn how to use Hugging Face's Transformers library for a wide variety of tasks such as text classification, named entity recognition, and question answering.

9.2 Tokenization with Hugging Face’s Transformers Library

Tokenization plays a fundamental role in any Natural Language Processing (NLP) task as it is the first step towards processing unstructured text data. It involves the process of breaking down the input text into smaller units called tokens, which can be understood by the machine learning models.

The tokenization process is used to establish the boundaries of a word, sentence, or a paragraph. It is important to note that different transformer models may require different kinds of tokenization techniques, which can be based on word, sub-word, or character-level tokenization, among others. Therefore, it is crucial to choose the appropriate tokenization method depending on the task at hand to ensure that the language model is trained accurately and effectively.

Hugging Face's Transformers library provides tokenizers for all the models it supports. These tokenizers do all the necessary preprocessing for the models' input data, such as:

  • Splitting the input into words, subwords, or symbols (like BERT's WordPiece or GPT-2's Byte-Pair Encoding (BPE)).
  • Mapping those tokens to their corresponding input IDs, which are integer values. Each unique token has a unique ID.
  • Creating the attention masks, which are binary tensors specifying which positions in the input the model should pay attention to.

Example:

Here's an example of how to use a tokenizer in Hugging Face’s Transformers library:

from transformers import BertTokenizer

# Initialize the tokenizer using pre-trained BERT Base Uncased model
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

# The sentence to be tokenized
sentence = 'Hello, I am learning about Hugging Face Transformers.'

# Use the tokenizer
tokens = tokenizer(sentence)

print(tokens)

This will output a dictionary with the tokenized input:

{'input_ids': [101, 7592, 1010, 1045, 2572, 4083, 2055, 17662, 2227, 19081, 1012, 102], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}

In the output:

  • input_ids are the unique IDs for each token in the sentence.
  • attention_mask is a binary tensor indicating the position of the padded indices so that the model does not attend to them.

This section covered how to tokenize your text data using Hugging Face’s Transformers Library. But, tokenization is just the beginning. In the next sections, we will learn how to use Hugging Face's Transformers library for a wide variety of tasks such as text classification, named entity recognition, and question answering.