💥 Fast State-of-the-Art Tokenizers optimized for Research and Production

Related tags

Text Data & NLPnlpnatural-language-processingtransformersgptlanguage-modelbertnatural-language-understanding
Overview



Build GitHub

Provides an implementation of today's most used tokenizers, with a focus on performance and versatility.

Main features:

  • Train new vocabularies and tokenize, using today's most used tokenizers.
  • Extremely fast (both training and tokenization), thanks to the Rust implementation. Takes less than 20 seconds to tokenize a GB of text on a server's CPU.
  • Easy to use, but also extremely versatile.
  • Designed for research and production.
  • Normalization comes with alignments tracking. It's always possible to get the part of the original sentence that corresponds to a given token.
  • Does all the pre-processing: Truncate, Pad, add the special tokens your model needs.

Bindings

We provide bindings to the following languages (more to come!):

Quick example using Python:

Choose your model between Byte-Pair Encoding, WordPiece or Unigram and instantiate a tokenizer:

from tokenizers import Tokenizer
from tokenizers.models import BPE

tokenizer = Tokenizer(BPE())

You can customize how pre-tokenization (e.g., splitting into words) is done:

from tokenizers.pre_tokenizers import Whitespace

tokenizer.pre_tokenizer = Whitespace()

Then training your tokenizer on a set of files just takes two lines of codes:

from tokenizers.trainers import BpeTrainer

trainer = BpeTrainer(special_tokens=["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]"])
tokenizer.train(files=["wiki.train.raw", "wiki.valid.raw", "wiki.test.raw"], trainer=trainer)

Once your tokenizer is trained, encode any text with just one line:

output = tokenizer.encode("Hello, y'all! How are you 😁 ?")
print(output.tokens)
# ["Hello", ",", "y", "'", "all", "!", "How", "are", "you", "[UNK]", "?"]

Check the python documentation or the python quicktour to learn more!

You might also like...
🤗 Transformers: State-of-the-art Natural Language Processing for Pytorch, TensorFlow, and JAX.
🤗 Transformers: State-of-the-art Natural Language Processing for Pytorch, TensorFlow, and JAX.

English | 简体中文 | 繁體中文 State-of-the-art Natural Language Processing for Jax, PyTorch and TensorFlow 🤗 Transformers provides thousands of pretrained mo

Hugging Face 77.2k Jan 3, 2023
Learn meanings behind words is a key element in NLP. This project concentrates on the disambiguation of preposition senses. Therefore, we train a bert-transformer model and surpass the state-of-the-art.

New State-of-the-Art in Preposition Sense Disambiguation Supervisor: Prof. Dr. Alexander Mehler Alexander Henlein Institutions: Goethe University TTLa

Dirk Neuhäuser 4 Apr 6, 2022
A model library for exploring state-of-the-art deep learning topologies and techniques for optimizing Natural Language Processing neural networks
A model library for exploring state-of-the-art deep learning topologies and techniques for optimizing Natural Language Processing neural networks

A Deep Learning NLP/NLU library by Intel® AI Lab Overview | Models | Installation | Examples | Documentation | Tutorials | Contributing NLP Architect

Intel Labs 2.9k Dec 31, 2022
Natural language processing summarizer using 3 state of the art Transformer models: BERT, GPT2, and T5
Natural language processing summarizer using 3 state of the art Transformer models: BERT, GPT2, and T5

NLP-Summarizer Natural language processing summarizer using 3 state of the art Transformer models: BERT, GPT2, and T5 This project aimed to provide in

Samuel Sharkey 1 Feb 7, 2022
Easy to use, state-of-the-art Neural Machine Translation for 100+ languages

EasyNMT - Easy to use, state-of-the-art Neural Machine Translation This package provides easy to use, state-of-the-art machine translation for more th

Ubiquitous Knowledge Processing Lab 748 Jan 6, 2023
A very simple framework for state-of-the-art Natural Language Processing (NLP)

A very simple framework for state-of-the-art NLP. Developed by Humboldt University of Berlin and friends. IMPORTANT: (30.08.2020) We moved our models

flair 12.3k Dec 31, 2022
State of the Art Natural Language Processing

Spark NLP: State of the Art Natural Language Processing Spark NLP is a Natural Language Processing library built on top of Apache Spark ML. It provide

John Snow Labs 3k Jan 5, 2023
A very simple framework for state-of-the-art Natural Language Processing (NLP)

A very simple framework for state-of-the-art NLP. Developed by Humboldt University of Berlin and friends. IMPORTANT: (30.08.2020) We moved our models

flair 10k Feb 18, 2021
State of the Art Natural Language Processing

Spark NLP: State of the Art Natural Language Processing Spark NLP is a Natural Language Processing library built on top of Apache Spark ML. It provide

John Snow Labs 1.9k Feb 18, 2021
Comments
  • Can't import any modules

    Can't import any modules

    What is says on the tin. Every module I try importing into a script is spitting out a "module not found" rror.

    Traceback (most recent call last): File "ab2.py", line 3, in from tokenizers.tools import BertWordPieceTokenizer ImportError: cannot import name 'BertWordPieceTokenizer' from 'tokenizers.tools' (/home/../anaconda3/envs/tokenizers/lib/python3.7/site-packages/tokenizers/tools/init.py)

    Traceback (most recent call last): File "ab2.py", line 3, in from transformers import BertWordPieceTokenizer ImportError: cannot import name 'BertWordPieceTokenizer' from 'transformers' (/home/../anaconda3/envs/tokenizers/lib/python3.7/site-packages/transformers/init.py)

    I've tried:

    import BertWordPieceTokenizer from tokenizers.toold import AutoTokenizer from tokenizers import BartTokenizer

    To Illustrate a few examples.

    I've installed Tokenizers in an anaconda3 venv via pip, via conda forge, and compiled from source.

    I've tried installing Transformers as well and get the same errors. I've tried installing Tokenizers and then installing Transformers and got the same errors.

    I've tried installing Transformers and then Tokenizers and gotten the same error.

    I've looked through the Tokenizers code and unless I'm missing something (entirely possible) autotokenize isn't even a part of the package? I'll admit I'm not a very experienced programmer but I'll be damned if I can find it.

    Help would be appreciated.

    System specs are:

    Linux mint 21.1 RTX2080 ti i78700k

    cudnn 8.1.1 cuda 11.2.0 Tensor rt 7.2.3 Python 3.7 (by the way, figuring out what was needed here, finding the files, and actually installing them was beyond arduous. There has to be a better way. It's the only way I could get anything at all to work though).

    opened by kronkinatorix 1
  • How to decode with the existing tokenizer

    How to decode with the existing tokenizer

    I train the tokenizer following the tutorial of the huggingface:

    from tokenizers import Tokenizer
from tokenizers.models import BPE
from tokenizers.trainers import BpeTrainer
from tokenizers.pre_tokenizers import Whitespace

tokenizer = Tokenizer(BPE(unk_token="[UNK]"))
trainer = BpeTrainer(special_tokens=["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]"])
tokenizer.pre_tokenizer = Whitespace()
files = [f"wikitext-103-raw/wiki.{split}.raw" for split in ["test", "train", "valid"]]
tokenizer.train(files, trainer)
tokenizer.save("tokenizer-wiki.json")

    But I don't know how to use the existing tokenizer for decoding:

    tokenizer = Tokenizer.from_file("tokenizer-wiki.json")
o=tokenizer.encode("sd jk sds  sds")
tokenizer.decode(o.ids)
# s d j k s ds s ds

    I know we can recover the output with the o.offsets, but what if we do not know the offsets, like we are decoding from a language model or NMT.

    opened by ZhiYuanZeng 4
  • Is there any support for 'google/tapas-mini-finetuned-wtq' tokenizer?

    Is there any support for 'google/tapas-mini-finetuned-wtq' tokenizer?

    I'm trying to run a tokenizer in java then eventually compile it to run on android for an open domain question and answer project. I'm wondering why 'google/tapas-mini-finetuned-wtq' doesn't work with DeepJavaLibrary. For more popular models the tokenizer is working. I'm assuming there is no fast tokenizer for tapas, so i was wondering if anyone had any advice on how to go about running tapas tokenizer and model on android/java?

    opened by memetrusidovski 4
  • OpenSSL internal error when importing tokenizers module

    OpenSSL internal error when importing tokenizers module

    When importing tokenizers 0.13.2 or 0.13.1 in a Fips mode enabled environment with Red Hat Enterprise Linux 8.6 (Ootpa) we see this error:

    sh-4.4# python3 -c "import tokenizers"
fips.c(145): OpenSSL internal error, assertion failed: FATAL FIPS SELFTEST FAILURE
Aborted (core dumped)

    Additional info:

    No errors when using tokenizers==0.13.0 or tokenizers==0.11.4
Python 3.8.13
OpenSSL 1.1.1g FIPS  21 Apr 2020 or OpenSSL 1.1.1k  FIPS 25 Mar 2021
    opened by wai25 3
Releases(v0.13.2)

  • v0.13.2(Nov 7, 2022)

  • python-v0.13.2(Nov 7, 2022)

  • node-v0.13.2(Nov 7, 2022)

  • v0.13.1(Oct 6, 2022)

  • python-v0.13.1(Oct 6, 2022)

  • node-v0.13.1(Oct 6, 2022)

  • python-v0.13.0(Sep 21, 2022)

    [0.13.0]

    • [#956] PyO3 version upgrade
    • [#1055] M1 automated builds
    • [#1008] Decoder is now a composable trait, but without being backward incompatible
    • [#1047, #1051, #1052] Processor is now a composable trait, but without being backward incompatible

    Both trait changes warrant a "major" number since, despite best efforts to not break backward compatibility, the code is different enough that we cannot be exactly sure.

    Source code(tar.gz)
    Source code(zip)

  • v0.13.0(Sep 19, 2022)

    [0.13.0]

    • [#1009] unstable_wasm feature to support building on Wasm (it's unstable !)
    • [#1008] Decoder is now a composable trait, but without being backward incompatible
    • [#1047, #1051, #1052] Processor is now a composable trait, but without being backward incompatible

    Both trait changes warrant a "major" number since, despite best efforts to not break backward compatibility, the code is different enough that we cannot be exactly sure.

    Source code(tar.gz)
    Source code(zip)

  • node-v0.13.0(Sep 19, 2022)

    [0.13.0]

    • [#1008] Decoder is now a composable trait, but without being backward incompatible
    • [#1047, #1051, #1052] Processor is now a composable trait, but without being backward incompatible
    Source code(tar.gz)
    Source code(zip)

  • python-v0.12.1(Apr 13, 2022)

  • v0.12.0(Mar 31, 2022)

    [0.12.0]

    Bump minor version because of a breaking change.

    The breaking change was causing more issues upstream in transformers than anticipated: https://github.com/huggingface/transformers/pull/16537#issuecomment-1085682657

    The decision was to rollback on that breaking change, and figure out a different way later to do this modification

    • [#938] Breaking change. Decoder trait is modified to be composable. This is only breaking if you are using decoders on their own. tokenizers should be error free.

    • [#939] Making the regex in ByteLevel pre_tokenizer optional (necessary for BigScience)

    • [#952] Fixed the vocabulary size of UnigramTrainer output (to respect added tokens)

    • [#954] Fixed not being able to save vocabularies with holes in vocab (ConvBert). Yell warnings instead, but stop panicking.

    • [#961] Added link for Ruby port of tokenizers

    • [#960] Feature gate for cli and its clap dependency

    Source code(tar.gz)
    Source code(zip)

  • python-v0.12.0(Mar 31, 2022)

    [0.12.0]

    The breaking change was causing more issues upstream in transformers than anticipated: https://github.com/huggingface/transformers/pull/16537#issuecomment-1085682657

    The decision was to rollback on that breaking change, and figure out a different way later to do this modification

    Bump minor version because of a breaking change.

    • [#938] Breaking change. Decoder trait is modified to be composable. This is only breaking if you are using decoders on their own. tokenizers should be error free.

    • [#939] Making the regex in ByteLevel pre_tokenizer optional (necessary for BigScience)

    • [#952] Fixed the vocabulary size of UnigramTrainer output (to respect added tokens)

    • [#954] Fixed not being able to save vocabularies with holes in vocab (ConvBert). Yell warnings instead, but stop panicking.

    • [#962] Fix tests for python 3.10

    • [#961] Added link for Ruby port of tokenizers

    Source code(tar.gz)
    Source code(zip)

  • node-v0.12.0(Mar 31, 2022)

    [0.12.0]

    The breaking change was causing more issues upstream in transformers than anticipated: https://github.com/huggingface/transformers/pull/16537#issuecomment-1085682657

    The decision was to rollback on that breaking change, and figure out a different way later to do this modification

    Bump minor version because of a breaking change. Using 0.12 to match other bindings.

    • [#938] Breaking change. Decoder trait is modified to be composable. This is only breaking if you are using decoders on their own. tokenizers should be error free.

    • [#939] Making the regex in ByteLevel pre_tokenizer optional (necessary for BigScience)

    • [#952] Fixed the vocabulary size of UnigramTrainer output (to respect added tokens)

    • [#954] Fixed not being able to save vocabularies with holes in vocab (ConvBert). Yell warnings instead, but stop panicking.

    • [#961] Added link for Ruby port of tokenizers

    Source code(tar.gz)
    Source code(zip)

  • v0.11.2(Feb 28, 2022)

  • python-v0.11.6(Feb 28, 2022)

  • node-v0.8.3(Feb 28, 2022)

  • python-v0.11.5(Feb 16, 2022)

  • v0.11.1(Jan 17, 2022)

    • [#882] Fixing Punctuation deserialize without argument.
    • [#868] Fixing missing direction in TruncationParams
    • [#860] Adding TruncationSide to TruncationParams
    Source code(tar.gz)
    Source code(zip)

  • python-v0.11.3(Jan 17, 2022)

    • [#882] Fixing Punctuation deserialize without argument.
    • [#868] Fixing missing direction in TruncationParams
    • [#860] Adding TruncationSide to TruncationParams
    Source code(tar.gz)
    Source code(zip)

  • node-v0.8.2(Jan 17, 2022)

  • node-v0.8.1(Jan 17, 2022)

  • python-v0.11.4(Jan 17, 2022)

  • python-v0.11.2(Jan 4, 2022)

  • python-v0.11.1(Dec 28, 2021)

  • python-v0.11.0(Dec 24, 2021)

    Fixed

    • [#585] Conda version should now work on old CentOS
    • [#844] Fixing interaction between is_pretokenized and trim_offsets.
    • [#851] Doc links

    Added

    • [#657]: Add SplitDelimiterBehavior customization to Punctuation constructor
    • [#845]: Documentation for Decoders.

    Changed

    • [#850]: Added a feature gate to enable disabling http features
    • [#718]: Fix WordLevel tokenizer determinism during training
    • [#762]: Add a way to specify the unknown token in SentencePieceUnigramTokenizer
    • [#770]: Improved documentation for UnigramTrainer
    • [#780]: Add Tokenizer.from_pretrained to load tokenizers from the Hugging Face Hub
    • [#793]: Saving a pretty JSON file by default when saving a tokenizer
    Source code(tar.gz)
    Source code(zip)

  • node-v0.8.0(Sep 2, 2021)

    BREACKING CHANGES

    • Many improvements on the Trainer (#519). The files must now be provided first when calling tokenizer.train(files, trainer).

    Features

    • Adding the TemplateProcessing
    • Add WordLevel and Unigram models (#490)
    • Add nmtNormalizer and precompiledNormalizer normalizers (#490)
    • Add templateProcessing post-processor (#490)
    • Add digitsPreTokenizer pre-tokenizer (#490)
    • Add support for mapping to sequences (#506)
    • Add splitPreTokenizer pre-tokenizer (#542)
    • Add behavior option to the punctuationPreTokenizer (#657)
    • Add the ability to load tokenizers from the Hugging Face Hub using fromPretrained (#780)

    Fixes

    • Fix a bug where long tokenizer.json files would be incorrectly deserialized (#459)
    • Fix RobertaProcessing deserialization in PostProcessorWrapper (#464)
    Source code(tar.gz)
    Source code(zip)

  • python-v0.10.3(May 24, 2021)

    Fixed

    • [#686]: Fix SPM conversion process for whitespace deduplication
    • [#707]: Fix stripping strings containing Unicode characters

    Added

    • [#693]: Add a CTC Decoder for Wave2Vec models

    Removed

    • [#714]: Removed support for Python 3.5
    Source code(tar.gz)
    Source code(zip)

  • python-v0.10.2(Apr 5, 2021)

    Fixed

    • [#652]: Fix offsets for Precompiled corner case
    • [#656]: Fix BPE continuing_subword_prefix
    • [#674]: Fix Metaspace serialization problems
    Source code(tar.gz)
    Source code(zip)

  • python-v0.10.1(Feb 4, 2021)

    Fixed

    • [#616]: Fix SentencePiece tokenizers conversion
    • [#617]: Fix offsets produced by Precompiled Normalizer (used by tokenizers converted from SPM)
    • [#618]: Fix Normalizer.normalize with PyNormalizedStringRefMut
    • [#620]: Fix serialization/deserialization for overlapping models
    • [#621]: Fix ByteLevel instantiation from a previously saved state (using __getstate__())
    Source code(tar.gz)
    Source code(zip)

  • python-v0.10.0(Jan 12, 2021)

    Added

    • [#508]: Add a Visualizer for notebooks to help understand how the tokenizers work
    • [#519]: Add a WordLevelTrainer used to train a WordLevel model
    • [#533]: Add support for conda builds
    • [#542]: Add Split pre-tokenizer to easily split using a pattern
    • [#544]: Ability to train from memory. This also improves the integration with datasets
    • [#590]: Add getters/setters for components on BaseTokenizer
    • [#574]: Add fust_unk option to SentencePieceBPETokenizer

    Changed

    • [#509]: Automatically stubbing the .pyi files
    • [#519]: Each Model can return its associated Trainer with get_trainer()
    • [#530]: The various attributes on each component can be get/set (ie. tokenizer.model.dropout = 0.1)
    • [#538]: The API Reference has been improved and is now up-to-date.

    Fixed

    • [#519]: During training, the Model is now trained in-place. This fixes several bugs that were forcing to reload the Model after a training.
    • [#539]: Fix BaseTokenizer enable_truncation docstring
    Source code(tar.gz)
    Source code(zip)
Owner
Hugging Face
Solving NLP, one commit at a time!
Hugging Face
GitHub Repository https://huggingface.co/docs/tokenizers
IndoBERTweet is the first large-scale pretrained model for Indonesian Twitter. Published at EMNLP 2021 (main conference)

IndoBERTweet 🐦 🇮🇩 1. Paper Fajri Koto, Jey Han Lau, and Timothy Baldwin. IndoBERTweet: A Pretrained Language Model for Indonesian Twitter with Effe

IndoLEM 40 Nov 30, 2022
nlabel is a library for generating, storing and retrieving tagging information and embedding vectors from various nlp libraries through a unified interface.

nlabel is a library for generating, storing and retrieving tagging information and embedding vectors from various nlp libraries through a unified interface.

Bernhard Liebl 2 Jun 10, 2022
profile tools for pytorch nn models

nnprof Introduction nnprof is a profile tool for pytorch neural networks. Features multi profile mode: nnprof support 4 profile mode: Layer level, Ope

Feng Wang 42 Jul 09, 2022
PyTorch implementation of "data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language" from Meta AI

data2vec-pytorch PyTorch implementation of "data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language" from Meta AI (F

Aryan Shekarlaban 105 Jan 04, 2023
Source code of paper "BP-Transformer: Modelling Long-Range Context via Binary Partitioning"

BP-Transformer This repo contains the code for our paper BP-Transformer: Modeling Long-Range Context via Binary Partition Zihao Ye, Qipeng Guo, Quan G

Zihao Ye 119 Nov 14, 2022
NLP codes implemented with Pytorch (w/o library such as huggingface)

NLP_scratch NLP codes implemented with Pytorch (w/o library such as huggingface) scripts ├── models: Neural Network models ├── data: codes for dataloa

3 Dec 28, 2021
💬 Open source machine learning framework to automate text- and voice-based conversations: NLU, dialogue management, connect to Slack, Facebook, and more - Create chatbots and voice assistants

Rasa Open Source Rasa is an open source machine learning framework to automate text-and voice-based conversations. With Rasa, you can build contextual

Rasa 15.3k Jan 03, 2023
Code associated with the Don't Stop Pretraining ACL 2020 paper

dont-stop-pretraining Code associated with the Don't Stop Pretraining ACL 2020 paper Citation @inproceedings{dontstoppretraining2020, author = {Suchi

AI2 449 Jan 04, 2023
Code for the paper "VisualBERT: A Simple and Performant Baseline for Vision and Language"

This repository contains code for the following two papers: VisualBERT: A Simple and Performant Baseline for Vision and Language (arxiv) with a short

Natural Language Processing @UCLA 464 Jan 04, 2023
This repository describes our reproducible framework for assessing self-supervised representation learning from speech

LeBenchmark: a reproducible framework for assessing SSL from speech Self-Supervised Learning (SSL) using huge unlabeled data has been successfully exp

49 Aug 24, 2022
PyTorch implementation of convolutional neural networks-based text-to-speech synthesis models

Deepvoice3_pytorch PyTorch implementation of convolutional networks-based text-to-speech synthesis models: arXiv:1710.07654: Deep Voice 3: Scaling Tex

Ryuichi Yamamoto 1.8k Dec 30, 2022
Conditional probing: measuring usable information beyond a baseline

Conditional probing: measuring usable information beyond a baseline

John Hewitt 20 Dec 15, 2022
Trains an OpenNMT PyTorch model and SentencePiece tokenizer.

Trains an OpenNMT PyTorch model and SentencePiece tokenizer. Designed for use with Argos Translate and LibreTranslate.

Argos Open Tech 61 Dec 13, 2022
Code for the paper "BERT Loses Patience: Fast and Robust Inference with Early Exit".

Patience-based Early Exit Code for the paper "BERT Loses Patience: Fast and Robust Inference with Early Exit". NEWS: We now have a better and tidier i

Kevin Canwen Xu 54 Jan 04, 2023
मराठी भाषा वाचविण्याचा एक प्रयास. इंग्रजी ते मराठीचा शब्दकोश. An attempt to preserve the Marathi language. A lightweight and ad free English to Marathi thesaurus.

For English, scroll down मराठी शब्द मराठी भाषा वाचवण्यासाठी मी हा ओपन सोर्स प्रोजेक्ट सुरू केला आहे. माझ्या मते, आपली भाषा हळूहळू आणि कोणाचाही लक्षात

मुक्त स्त्रोत 20 Oct 11, 2022
A desktop GUI providing an audio interface for GPT3.

Jabberwocky neil_degrasse_tyson_with_audio.mp4 Project Description This GUI provides an audio interface to GPT-3. My main goal was to provide a conven

16 Nov 27, 2022
Code and datasets for our paper "PTR: Prompt Tuning with Rules for Text Classification"

PTR Code and datasets for our paper "PTR: Prompt Tuning with Rules for Text Classification" If you use the code, please cite the following paper: @art

THUNLP 118 Dec 30, 2022
Facilitating the design, comparison and sharing of deep text matching models.

MatchZoo Facilitating the design, comparison and sharing of deep text matching models. MatchZoo 是一个通用的文本匹配工具包，它旨在方便大家快速的实现、比较、以及分享最新的深度文本匹配模型。 🔥 News

Neural Text Matching Community 3.7k Jan 02, 2023
Speech to text streamlit app

Speech to text Streamlit-app! 👄 This speech to text recognition is powered by t

Charly Wargnier 9 Jan 01, 2023
A minimal Conformer ASR implementation adapted from ESPnet.

Conformer ASR A minimal Conformer ASR implementation adapted from ESPnet. Introduction I want to use the pre-trained English ASR model provided by ESP

Niu Zhe 3 Jan 24, 2022