What is Tokenizer in transformers?

This recipe explains what is Tokenizer in transformers.

Recipe Objective - What is Tokenizer in transformers?

The tokenizer is responsible for preparing input for the model. The library contains the markers for all models. Most tokenizers have two versions: a full Python implementation and a "fast" implementation supported by the Rust library tokenizer. The "fast" implementation allows:

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1. significant speedup, especially when performing batch tokenization and
2. additional mapping methods between the original string (characters and words) and the token space (for example, get the index of the token containing a certain character) or the range of characters corresponding to a specific tag). SentencePiece-based tokenizers are currently ineligible for the "quick" implementation (applicable to T5, ALBERT, CamemBERT, XLMRoBERTa, and XLNet models).

Types of tokenizer:

1. PreTrainedTokenizer
2. PreTrainedTokenizerFast
3. BatchEncoding

For more related projects -

/projects/data-science-projects/tensorflow-projects
/projects/data-science-projects/keras-deep-learning-projects

Example -

Let's see how to make a tokenizer in transformers:

# Importing libraries
from transformers import BertTokenizer

# Creating tokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
tokenizer

Output -
PreTrainedTokenizer(name_or_path='bert-base-uncased', vocab_size=30522, model_max_len=512, is_fast=False, padding_side='right', special_tokens={'unk_token': '[UNK]', 'sep_token': '[SEP]', 'pad_token': '[PAD]', 'cls_token': '[CLS]', 'mask_token': '[MASK]'})

In this way, we can make a tokenizer in transformers.

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