Tokenization

What is Tokenization?

Tokenization is the process of breaking down unstructured text into smaller units called tokens. These tokens can be words, subwords, or even characters, depending on the tokenization strategy used. For instance, in a word-based tokenization approach, the sentence "I love AI" would be split into three tokens: ["I", "love", "AI"]. In contrast, subword tokenization might break "unhappiness" into ["un", "happiness"].

Importance of Tokenization in SLM Training

Tokenization serves several critical purposes in the context of SLM training:

• Standardization: It converts diverse text inputs into a uniform format, allowing the model to process different types of text consistently.

• Vocabulary Management: Tokenization helps in creating a vocabulary that the model can understand, which is essential for mapping tokens to numerical representations (embeddings).

• Handling Out-of-Vocabulary (OOV) Words: By employing techniques like subword tokenization, models can effectively deal with OOV words, enhancing their ability to understand and generate text.

• Efficiency: Proper tokenization reduces the complexity of the input data, which can lead to faster training times and lower computational costs.

The Tokenization Process

The tokenization process in training SLMs typically involves the following steps:

Text Preprocessing

• Before tokenization, text data often undergoes preprocessing, which may include:

• Lowercasing: Converting all characters to lowercase to maintain uniformity.

• Removing Punctuation: Eliminating unnecessary punctuation marks that do not contribute to meaning.

• Whitespace Normalization: Ensuring consistent spacing between words.

Tokenization Techniques

Different tokenization techniques can be employed based on the model's requirements:

• Word-based Tokenization: Splits text into individual words. This method is simple but can struggle with OOV words.

• Subword Tokenization: Techniques like Byte Pair Encoding (BPE) or WordPiece break words into smaller subword units, allowing the model to handle OOV words more effectively. For example, "unhappiness" might be tokenized into ["un", "happi", "ness"].

• Character-based Tokenization: Each character is treated as a token. This method can capture all possible inputs but may lead to longer sequences and increased complexity.

Mapping Tokens to IDs

Once the text is tokenized, each token is mapped to a unique identifier (ID) using a predefined vocabulary. This mapping is crucial for converting text into numerical representations that can be fed into the model.

Padding and Truncation

To ensure that all input sequences are of the same length, padding (adding special tokens to the end of shorter sequences) and truncation (cutting longer sequences) are applied. This step is essential for batch processing during training.

Creating Input Features

Finally, the tokenized and processed data is transformed into input features suitable for the model. This may include attention masks that indicate which tokens are padding and which are actual data.

Conclusion

Tokenization is a crucial step in the training of Small Language Models, enabling them to effectively understand and generate human language. By choosing appropriate tokenization techniques and preprocessing methods, developers can enhance the performance and efficiency of SLMs, making them suitable for various applications in natural language processing. The careful design of the tokenization process contributes significantly to the overall success of training these models, ensuring they can learn from and adapt to the complexities of human language. 7, 8, 9