How are tokens processed by ML models?

After you perform tokenization, the numerical identifiers representing each token are typically converted into embeddings before being processed by ML models. Embeddings are dense, lower-dimensional vector representations of tokens that capture semantic and syntactic information about the token's meaning and context within the text.

The process typically works as follows:

• Tokenization: The text is tokenized into individual tokens, and each token is assigned a unique numerical identifier.

• Embedding Lookup: Each numerical token identifier is then looked up in an embedding matrix or embedding table. This table contains pre-trained embeddings for each token in the vocabulary.

• Embedding Representation: The numerical token identifier is replaced by its corresponding embedding vector retrieved from the embedding table. This results in a sequence of dense embedding vectors, with each vector representing a token in the text.

• Embedding Matrix: The embedding matrix is usually learned during the training process of the model, where the model learns to update the embeddings based on the task and the input data. Alternatively, pre-trained embeddings can be used, which are trained on large corpora of text data (e.g., Word2Vec, GloVe, or embeddings from transformer-based models like BERT or GPT).

• Model Processing: The sequence of embedding vectors is then processed by the machine learning model (e.g., recurrent neural network, convolutional neural network, or transformer). The model learns to extract meaningful features from the embeddings and performs tasks such as classification, regression, or sequence generation.

Embeddings play a crucial role in natural language processing tasks as they help capture the semantic and contextual information of tokens, enabling the model to better understand and process the textual data.