Turning tokens into embeddings
Embeddings are numerical representations of words, phrases, or entire documents in a high-dimensional vector space. Essentially, we represent words as arrays of numbers to capture their semantic meaning. These numerical arrays aim to encode the underlying significance of words and sentences, allowing models to understand and process text in a meaningful way. Let’s explore the process from tokenization to embedding.
The process starts with tokenization, whereby text is split into manageable units called tokens. For instance, the sentence “The cat sat on the mat” might be tokenized into individual words or subword units such as [“The”, “cat”, “sat”, “on”, “the”, “mat”]. Once the text is tokenized, each token is mapped to an embedding vector using an embedding layer or lookup table. This table is often initialized with random values and then trained...
