Tokenization

Tokenization is a fundamental concept in the training of large language models.

The process breaks down text into smaller, manageable units called tokens. These tokens, which can range from individual characters to entire words, enable neural models to better understand and process human language.

Tokenization can be a complex process, as it involves handling different types of text data, such as punctuation, numbers, and special characters, and determining how to split them into meaningful units.

Tokenization can also vary depending on the specific task or application. For example, in some cases, it may be necessary to split words into smaller subwords to handle out-of-vocabulary (OOV) words that are not present in a pre-trained vocabulary.

Once text has been tokenized, it can be further processed using techniques such as stemming, lemmatization, or part-of-speech tagging, or fed into a machine learning model for training or inference.

Tokenization Process

Segmentation: The first step of tokenization involves breaking down text into units. These units can be as large as sentences, as small as characters, or more commonly, words and subwords.

Vocabulary Building: Once you decide on the granularity of the units (words, subwords, etc.), you create a vocabulary or a list of unique tokens from the corpus.

Mapping: Each unique token in the vocabulary is assigned a unique integer ID.

Encoding: The original text is then converted or "encoded" into a sequence of these integer IDs according to the mapping. For example, if you tokenize the sentence "I love AI", and your vocabulary mapping is {'I': 1, 'love': 2, 'AI': 3}, the encoded sentence becomes [1, 2, 3].

Here's a general outline of how to tokenize an entire dataset

Choose a tokenization method

Depending on the language, dataset, and the specific requirements of your task, select an appropriate tokenization method. This could be word-based, subword-based (e.g., BPE, WordPiece, or SentencePiece), or character-based tokenization.

Before tokenizing, clean and pre-process the dataset to ensure consistency and remove any irrelevant information. This might involve converting the text to lowercase, removing special characters or punctuation, or handling contractions and abbreviations.

Train the tokenizer

If you're using a data-driven tokenization method like BPE, WordPiece, or SentencePiece, you need to train the tokenizer on your dataset. This step allows the tokenizer to learn the most frequent and meaningful tokens in the data.

Tokenize the dataset

Once the tokenizer is trained, apply it to the entire dataset. The tokenizer will break down the text into smaller units according to the chosen method. For instance, it may convert sentences into lists of words or subword tokens.

Post-processing

After tokenization, you may want to perform additional processing steps such as adding special tokens (e.g., [CLS], [SEP], or [MASK] in BERT), padding sequences to a fixed length, or creating batches for input to your NLP model.

Save the tokenized dataset

Finally, save the tokenized dataset for further use in training or evaluation tasks.

Depending on the downstream task, you may want to save the dataset in a specific format (e.g., PyTorch tensors, TensorFlow tensors, or NumPy arrays).