How does a BERT tokenizer work?
The BERT model receives a fixed length of sentence as input. Usually the maximum length of a sentence depends on the data we are working on. For sentences that are shorter than this maximum length, we will have to add paddings (empty tokens) to the sentences to make up the length.
What tokenization does BERT use?
The PyTorch-Pretrained-BERT library provides us with tokenizer for each of BERTS models. Here we use the basic bert-base-uncased model, there are several other models, including much larger models. Maximum sequence size for BERT is 512, so we’ll truncate any review that is longer than this.
Which is better Bert or GPT-2 for English?
It seems that if you want normal left-to-right generation in English, GPT-2 is still the best way to go. BERT’s main strength is NLP tasks, and the variety of languages for which a pre-trained model is available. If you’ve used this overview to help you choose a language model, let me know in the comments.
How does Bert tokenization work in a corpus?
The BERT tokenization function, on the other hand, will first breaks the word into two subwoards, namely characteristic and ##ally, where the first token is a more commonly-seen word (prefix) in a corpus, and the second token is prefixed by two hashes ## to indicate that it is a suffix following some other subwords.
How are tokens used in the Bert model?
In the original implementation, the token [PAD] is used to represent paddings to the sentence. When the BERT model was trained, each token was given a unique ID. Hence, when we want to use a pre-trained BERT model, we will first need to convert each token in the input sentence into its corresponding unique IDs.
How are unseen tokens converted into unseen words in Bert?
However, converting all unseen tokens into [UNK] will take away a lot of information from the input data. Hence, BERT makes use of a WordPiece algorithm that breaks a word into several subwords, such that commonly seen subwords can also be represented by the model.