Summary

In this chapter, we measured the impact of tokenization on the subsequent layers of a transformer model. A transformer model can only attend to tokens from a stack's embedding and positional encoding sub-layers. It does not matter if the model is an encoder-decoder, encoder-only, or decoder-only model. Furthermore, whether the dataset seems good enough to train does not matter.If the tokenization process fails, even partly, our transformer model will miss critical tokens.We first saw that raw datasets might be enough for standard language tasks to train a transformer.However, we discovered that even if a pretrained tokenizer has gone through a billion words, it only creates a dictionary with a small portion of the vocabulary it comes across. Like us, a tokenizer captures the essence of the language it is learning and only remembers the most important words if these words are also frequently used. This approach works well for a standard task and creates problems...