How to'Tokenize' the data?

How to 'Tokenize' the data?

Tokenization is a critical step in preparing your data for training a language model like GPT. It involves converting the raw text into tokens, which are the smallest units the model can understand. These tokens can be words, subwords, or even characters, depending on the tokenization algorithm used. Here's a general guide to tokenization:

1. Choose a Tokenization Algorithm

• Word-Level Tokenization: Splits the text into words. Simple but can lead to a large vocabulary.

• Subword Tokenization: Splits words into smaller units (subwords). BPE (Byte Pair Encoding), SentencePiece, and WordPiece are popular subword tokenization methods. They balance vocabulary size and out-of-vocabulary issues effectively.

• Character-Level Tokenization: Treats each character as a token. Useful for languages with a large number of characters but less efficient for languages like English.

2. Implementing Tokenization

• Use Pre-built Tokenizers: Libraries like NLTK, SpaCy, or Hugging Face's Transformers provide pre-built tokenizers.

• Custom Tokenization: You can also build a custom tokenizer, especially if your text has unique requirements (like legal documents).

3. Preprocessing Text

• Clean Your Text: Before tokenizing, clean your data. This includes removing unnecessary whitespace, punctuation, and possibly standardizing case.

• Special Characters: In legal texts, consider how to handle special characters or terms that are unique to legal language.

4. Running the Tokenizer

• Apply the Tokenizer: Run your chosen tokenizer on the dataset. This will convert your text into a sequence of tokens.

• Store Tokens: Store the tokenized output in a format suitable for training (like arrays or tensors).

5. Handling Out-of-Vocabulary Words

• OOV Tokens: Decide how to handle words that the tokenizer has not seen before. Subword tokenization helps mitigate this issue.

6. Building a Vocabulary

• Create a Vocabulary List: The tokenizer will create a vocabulary of all unique tokens. In the case of subword tokenization, this list includes subwords.

7. Token IDs

• Convert Tokens to IDs: Each token is mapped to a unique ID. These IDs are what the model actually processes.

8. Testing and Iteration

• Test the Tokenizer: Apply the tokenizer to a sample of your data to see if it's working as expected.

• Iterate: Based on this test, you might need to adjust your tokenization strategy.

Example with Hugging Face Transformers:

pythonCopy codefrom transformers import BertTokenizer

# Load pre-trained tokenizer or initialize a new one
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

# Tokenize text
input_text = "Example legal text here."
tokens = tokenizer.tokenize(input_text)

# Convert tokens to token IDs
token_ids = tokenizer.convert_tokens_to_ids(tokens)

Remember, the choice of tokenizer and the specifics of implementation can depend greatly on your specific dataset and the language model you are using. For legal texts, it's important to ensure that the tokenizer can handle the specific terminology and structure of legal language effectively.