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

Chapter 7: Prominent Transformer Models and Their Applications

7.2 Tokenization Specifics with Transformers

Before we feed our text data into a transformer model such as BERT, we need to convert it into a format the model can understand. This process is called tokenization. The tokenization must be performed using the same tokenization method and vocabulary that were used when the model was trained.

Tokenization is the process of breaking up text into smaller, meaningful pieces called tokens. These tokens serve as inputs to machine learning models like BERT, allowing them to "understand" the text.

Transformers, like BERT, come equipped with their own tokenizers designed specifically for their architecture. In the case of BERT, it uses a tokenizer based on WordPiece, which is particularly effective at handling out-of-vocabulary words. By using BERT's built-in tokenizer, we can ensure that our inputs are preprocessed in a way that best suits the model's architecture and capabilities.

The tokenizer has a few key steps:

Text normalization

In order to ensure consistency across all text data, it is important to normalize the text. This process involves converting all text to lowercase and removing any special characters that do not carry any meaningful information. By doing so, we can better analyze and compare different text data sets, which in turn can help us gain valuable insights and make more informed decisions based on the data.

Additionally, text normalization can also improve the accuracy of natural language processing applications, such as sentiment analysis or text classification, by reducing the complexity of the text and making it easier for the algorithms to work with. Therefore, incorporating text normalization into our data processing pipeline is an essential step towards achieving better results and extracting more value from our data.

Tokenization

The text is split into tokens, which could be as short as one character or as long as one word. Tokenization is a crucial step in natural language processing. It involves splitting the text into individual tokens, which could be as small as a single character or as large as a word. The goal of tokenization is to break down the text into its basic components so that it can be more easily analyzed and processed by machines.

Once the text has been tokenized, various techniques can be used to further analyze it, such as part-of-speech tagging, named entity recognition, and sentiment analysis. Therefore, it is important to ensure that the tokenization process is accurate and effective in order to obtain meaningful results from natural language processing applications.

WordPiece tokenization

This is a process that is used by some natural language processing models to better handle out-of-vocabulary words. Essentially, any words that are not in the model's pre-defined vocabulary are further split into subwords that are in the vocabulary. This can allow the model to better understand the meaning of the word, even if it has not seen it before.

As an example, let's say that the word 'embeddings' is not in the model's vocabulary. Through WordPiece tokenization, the word can be split into ['em', '##bed', '##ding', '##s'] which are all in the vocabulary. This can help the model to more accurately represent the meaning of the word, even if it is not explicitly defined in the vocabulary.

Example:

Here's an example of how to tokenize sentences for BERT:

from transformers import BertTokenizer

tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
sentences = ["This is a positive sentence.", "This is a negative sentence."]

inputs = tokenizer(sentences, padding=True, truncation=True, return_tensors="pt")

The inputs object now contains the following entries: input_idsattention_mask, and token_type_ids.

  • input_ids: These are the indices corresponding to each token in our sentence within the BERT tokenizer vocabulary.
  • attention_mask: This tells the model which tokens should be attended to and which should not. This is important for padding and for models like BERT that use masked self-attention.
  • token_type_ids: These are used to distinguish between the two sentences in Next Sentence Prediction tasks (which BERT is pre-trained on). Most tasks only require one sentence input, so you can largely ignore this output.

7.2 Tokenization Specifics with Transformers

Before we feed our text data into a transformer model such as BERT, we need to convert it into a format the model can understand. This process is called tokenization. The tokenization must be performed using the same tokenization method and vocabulary that were used when the model was trained.

Tokenization is the process of breaking up text into smaller, meaningful pieces called tokens. These tokens serve as inputs to machine learning models like BERT, allowing them to "understand" the text.

Transformers, like BERT, come equipped with their own tokenizers designed specifically for their architecture. In the case of BERT, it uses a tokenizer based on WordPiece, which is particularly effective at handling out-of-vocabulary words. By using BERT's built-in tokenizer, we can ensure that our inputs are preprocessed in a way that best suits the model's architecture and capabilities.

The tokenizer has a few key steps:

Text normalization

In order to ensure consistency across all text data, it is important to normalize the text. This process involves converting all text to lowercase and removing any special characters that do not carry any meaningful information. By doing so, we can better analyze and compare different text data sets, which in turn can help us gain valuable insights and make more informed decisions based on the data.

Additionally, text normalization can also improve the accuracy of natural language processing applications, such as sentiment analysis or text classification, by reducing the complexity of the text and making it easier for the algorithms to work with. Therefore, incorporating text normalization into our data processing pipeline is an essential step towards achieving better results and extracting more value from our data.

Tokenization

The text is split into tokens, which could be as short as one character or as long as one word. Tokenization is a crucial step in natural language processing. It involves splitting the text into individual tokens, which could be as small as a single character or as large as a word. The goal of tokenization is to break down the text into its basic components so that it can be more easily analyzed and processed by machines.

Once the text has been tokenized, various techniques can be used to further analyze it, such as part-of-speech tagging, named entity recognition, and sentiment analysis. Therefore, it is important to ensure that the tokenization process is accurate and effective in order to obtain meaningful results from natural language processing applications.

WordPiece tokenization

This is a process that is used by some natural language processing models to better handle out-of-vocabulary words. Essentially, any words that are not in the model's pre-defined vocabulary are further split into subwords that are in the vocabulary. This can allow the model to better understand the meaning of the word, even if it has not seen it before.

As an example, let's say that the word 'embeddings' is not in the model's vocabulary. Through WordPiece tokenization, the word can be split into ['em', '##bed', '##ding', '##s'] which are all in the vocabulary. This can help the model to more accurately represent the meaning of the word, even if it is not explicitly defined in the vocabulary.

Example:

Here's an example of how to tokenize sentences for BERT:

from transformers import BertTokenizer

tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
sentences = ["This is a positive sentence.", "This is a negative sentence."]

inputs = tokenizer(sentences, padding=True, truncation=True, return_tensors="pt")

The inputs object now contains the following entries: input_idsattention_mask, and token_type_ids.

  • input_ids: These are the indices corresponding to each token in our sentence within the BERT tokenizer vocabulary.
  • attention_mask: This tells the model which tokens should be attended to and which should not. This is important for padding and for models like BERT that use masked self-attention.
  • token_type_ids: These are used to distinguish between the two sentences in Next Sentence Prediction tasks (which BERT is pre-trained on). Most tasks only require one sentence input, so you can largely ignore this output.

7.2 Tokenization Specifics with Transformers

Before we feed our text data into a transformer model such as BERT, we need to convert it into a format the model can understand. This process is called tokenization. The tokenization must be performed using the same tokenization method and vocabulary that were used when the model was trained.

Tokenization is the process of breaking up text into smaller, meaningful pieces called tokens. These tokens serve as inputs to machine learning models like BERT, allowing them to "understand" the text.

Transformers, like BERT, come equipped with their own tokenizers designed specifically for their architecture. In the case of BERT, it uses a tokenizer based on WordPiece, which is particularly effective at handling out-of-vocabulary words. By using BERT's built-in tokenizer, we can ensure that our inputs are preprocessed in a way that best suits the model's architecture and capabilities.

The tokenizer has a few key steps:

Text normalization

In order to ensure consistency across all text data, it is important to normalize the text. This process involves converting all text to lowercase and removing any special characters that do not carry any meaningful information. By doing so, we can better analyze and compare different text data sets, which in turn can help us gain valuable insights and make more informed decisions based on the data.

Additionally, text normalization can also improve the accuracy of natural language processing applications, such as sentiment analysis or text classification, by reducing the complexity of the text and making it easier for the algorithms to work with. Therefore, incorporating text normalization into our data processing pipeline is an essential step towards achieving better results and extracting more value from our data.

Tokenization

The text is split into tokens, which could be as short as one character or as long as one word. Tokenization is a crucial step in natural language processing. It involves splitting the text into individual tokens, which could be as small as a single character or as large as a word. The goal of tokenization is to break down the text into its basic components so that it can be more easily analyzed and processed by machines.

Once the text has been tokenized, various techniques can be used to further analyze it, such as part-of-speech tagging, named entity recognition, and sentiment analysis. Therefore, it is important to ensure that the tokenization process is accurate and effective in order to obtain meaningful results from natural language processing applications.

WordPiece tokenization

This is a process that is used by some natural language processing models to better handle out-of-vocabulary words. Essentially, any words that are not in the model's pre-defined vocabulary are further split into subwords that are in the vocabulary. This can allow the model to better understand the meaning of the word, even if it has not seen it before.

As an example, let's say that the word 'embeddings' is not in the model's vocabulary. Through WordPiece tokenization, the word can be split into ['em', '##bed', '##ding', '##s'] which are all in the vocabulary. This can help the model to more accurately represent the meaning of the word, even if it is not explicitly defined in the vocabulary.

Example:

Here's an example of how to tokenize sentences for BERT:

from transformers import BertTokenizer

tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
sentences = ["This is a positive sentence.", "This is a negative sentence."]

inputs = tokenizer(sentences, padding=True, truncation=True, return_tensors="pt")

The inputs object now contains the following entries: input_idsattention_mask, and token_type_ids.

  • input_ids: These are the indices corresponding to each token in our sentence within the BERT tokenizer vocabulary.
  • attention_mask: This tells the model which tokens should be attended to and which should not. This is important for padding and for models like BERT that use masked self-attention.
  • token_type_ids: These are used to distinguish between the two sentences in Next Sentence Prediction tasks (which BERT is pre-trained on). Most tasks only require one sentence input, so you can largely ignore this output.

7.2 Tokenization Specifics with Transformers

Before we feed our text data into a transformer model such as BERT, we need to convert it into a format the model can understand. This process is called tokenization. The tokenization must be performed using the same tokenization method and vocabulary that were used when the model was trained.

Tokenization is the process of breaking up text into smaller, meaningful pieces called tokens. These tokens serve as inputs to machine learning models like BERT, allowing them to "understand" the text.

Transformers, like BERT, come equipped with their own tokenizers designed specifically for their architecture. In the case of BERT, it uses a tokenizer based on WordPiece, which is particularly effective at handling out-of-vocabulary words. By using BERT's built-in tokenizer, we can ensure that our inputs are preprocessed in a way that best suits the model's architecture and capabilities.

The tokenizer has a few key steps:

Text normalization

In order to ensure consistency across all text data, it is important to normalize the text. This process involves converting all text to lowercase and removing any special characters that do not carry any meaningful information. By doing so, we can better analyze and compare different text data sets, which in turn can help us gain valuable insights and make more informed decisions based on the data.

Additionally, text normalization can also improve the accuracy of natural language processing applications, such as sentiment analysis or text classification, by reducing the complexity of the text and making it easier for the algorithms to work with. Therefore, incorporating text normalization into our data processing pipeline is an essential step towards achieving better results and extracting more value from our data.

Tokenization

The text is split into tokens, which could be as short as one character or as long as one word. Tokenization is a crucial step in natural language processing. It involves splitting the text into individual tokens, which could be as small as a single character or as large as a word. The goal of tokenization is to break down the text into its basic components so that it can be more easily analyzed and processed by machines.

Once the text has been tokenized, various techniques can be used to further analyze it, such as part-of-speech tagging, named entity recognition, and sentiment analysis. Therefore, it is important to ensure that the tokenization process is accurate and effective in order to obtain meaningful results from natural language processing applications.

WordPiece tokenization

This is a process that is used by some natural language processing models to better handle out-of-vocabulary words. Essentially, any words that are not in the model's pre-defined vocabulary are further split into subwords that are in the vocabulary. This can allow the model to better understand the meaning of the word, even if it has not seen it before.

As an example, let's say that the word 'embeddings' is not in the model's vocabulary. Through WordPiece tokenization, the word can be split into ['em', '##bed', '##ding', '##s'] which are all in the vocabulary. This can help the model to more accurately represent the meaning of the word, even if it is not explicitly defined in the vocabulary.

Example:

Here's an example of how to tokenize sentences for BERT:

from transformers import BertTokenizer

tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
sentences = ["This is a positive sentence.", "This is a negative sentence."]

inputs = tokenizer(sentences, padding=True, truncation=True, return_tensors="pt")

The inputs object now contains the following entries: input_idsattention_mask, and token_type_ids.

  • input_ids: These are the indices corresponding to each token in our sentence within the BERT tokenizer vocabulary.
  • attention_mask: This tells the model which tokens should be attended to and which should not. This is important for padding and for models like BERT that use masked self-attention.
  • token_type_ids: These are used to distinguish between the two sentences in Next Sentence Prediction tasks (which BERT is pre-trained on). Most tasks only require one sentence input, so you can largely ignore this output.