75 lines
2.8 KiB
Markdown
75 lines
2.8 KiB
Markdown
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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# Use tokenizers from 🤗 Tokenizers
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The [`PreTrainedTokenizerFast`] depends on the [🤗 Tokenizers](https://huggingface.co/docs/tokenizers) library. The tokenizers obtained from the 🤗 Tokenizers library can be
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loaded very simply into 🤗 Transformers.
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Before getting in the specifics, let's first start by creating a dummy tokenizer in a few lines:
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```python
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>>> from tokenizers import Tokenizer
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>>> from tokenizers.models import BPE
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>>> from tokenizers.trainers import BpeTrainer
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>>> from tokenizers.pre_tokenizers import Whitespace
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>>> tokenizer = Tokenizer(BPE(unk_token="[UNK]"))
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>>> trainer = BpeTrainer(special_tokens=["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]"])
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>>> tokenizer.pre_tokenizer = Whitespace()
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>>> files = [...]
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>>> tokenizer.train(files, trainer)
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```
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We now have a tokenizer trained on the files we defined. We can either continue using it in that runtime, or save it to
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a JSON file for future re-use.
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## Loading directly from the tokenizer object
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Let's see how to leverage this tokenizer object in the 🤗 Transformers library. The
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[`PreTrainedTokenizerFast`] class allows for easy instantiation, by accepting the instantiated
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*tokenizer* object as an argument:
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```python
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>>> from transformers import PreTrainedTokenizerFast
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>>> fast_tokenizer = PreTrainedTokenizerFast(tokenizer_object=tokenizer)
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```
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This object can now be used with all the methods shared by the 🤗 Transformers tokenizers! Head to [the tokenizer
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page](main_classes/tokenizer) for more information.
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## Loading from a JSON file
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In order to load a tokenizer from a JSON file, let's first start by saving our tokenizer:
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```python
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>>> tokenizer.save("tokenizer.json")
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```
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The path to which we saved this file can be passed to the [`PreTrainedTokenizerFast`] initialization
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method using the `tokenizer_file` parameter:
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```python
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>>> from transformers import PreTrainedTokenizerFast
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>>> fast_tokenizer = PreTrainedTokenizerFast(tokenizer_file="tokenizer.json")
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```
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This object can now be used with all the methods shared by the 🤗 Transformers tokenizers! Head to [the tokenizer
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page](main_classes/tokenizer) for more information.
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