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71 lines
2.1 KiB
ReStructuredText
71 lines
2.1 KiB
ReStructuredText
Tokenizers
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====================================================================================================
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Fast State-of-the-art tokenizers, optimized for both research and production
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`🤗 Tokenizers`_ provides an implementation of today's most used tokenizers, with
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a focus on performance and versatility. These tokenizers are also used in
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`🤗 Transformers`_.
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.. _🤗 Tokenizers: https://github.com/huggingface/tokenizers
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.. _🤗 Transformers: https://github.com/huggingface/transformers
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Main features:
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----------------------------------------------------------------------------------------------------
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- Train new vocabularies and tokenize, using today's most used tokenizers.
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- Extremely fast (both training and tokenization), thanks to the Rust implementation. Takes
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less than 20 seconds to tokenize a GB of text on a server's CPU.
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- Easy to use, but also extremely versatile.
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- Designed for both research and production.
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- Full alignment tracking. Even with destructive normalization, it's always possible to get
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the part of the original sentence that corresponds to any token.
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- Does all the pre-processing: Truncation, Padding, add the special tokens your model needs.
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.. toctree::
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:maxdepth: 2
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:caption: Getting Started
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quicktour
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installation/main
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pipeline
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components
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.. toctree::
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:maxdepth: 3
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:caption: API Reference
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api/reference
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.. entities:: python
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:global:
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class
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class
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classmethod
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class method
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Tokenizer
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:class:`~tokenizers.Tokenizer`
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Tokenizer.train
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:meth:`~tokenizers.Tokenizer.train`
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Tokenizer.save
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:meth:`~tokenizers.Tokenizer.save`
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Tokenizer.from_file
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:meth:`~tokenizers.Tokenizer.from_file`
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.. entities:: rust
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:global:
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class
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struct
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classmethod
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static method
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Tokenizer
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`Tokenizer <https://docs.rs/tokenizers/latest/tokenizers/tokenizer/struct.Tokenizer.html>`__
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Tokenizer.train
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`train <https://docs.rs/tokenizers/0.10.1/tokenizers/tokenizer/struct.Tokenizer.html#method.train>`__
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