💥 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
Search-Engine - 📖 AI based search engine

Search Engine AI based search engine that was trained on 25000 samples, feel free to train on up to 1.2M sample from kaggle dataset, link below StackS

Vladislav Kruglikov 2 Nov 29, 2022
multi-label，classifier，text classification，多标签文本分类，文本分类，BERT，ALBERT，multi-label-classification，seq2seq，attention，beam search

multi-label，classifier，text classification，多标签文本分类，文本分类，BERT，ALBERT，multi-label-classification，seq2seq，attention，beam search

hellonlp 30 Dec 12, 2022
Download videos from YouTube/Twitch/Twitter right in the Windows Explorer, without installing any shady shareware apps

youtube-dl and ffmpeg Windows Explorer Integration Download videos from YouTube/Twitch/Twitter and more (any platform that is supported by youtube-dl)

Wolfgang 226 Dec 30, 2022
Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning

GenSen Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning Sandeep Subramanian, Adam Trischler, Yoshua B

Maluuba Inc. 309 Oct 19, 2022
Official source for spanish Language Models and resources made @ BSC-TEMU within the "Plan de las Tecnologías del Lenguaje" (Plan-TL).

Spanish Language Models 💃🏻 Corpora 📃 Corpora Number of documents Size (GB) BNE 201,080,084 570GB Models 🤖 RoBERTa-base BNE: https://huggingface.co

PlanTL-SANIDAD 203 Dec 20, 2022
Some embedding layer implementation using ivy library

ivy-manual-embeddings Some embedding layer implementation using ivy library. Just for fun. It is based on NYCTaxiFare dataset from kaggle (cut down to

Ishtiaq Hussain 2 Feb 10, 2022
text to speech toolkit. 好用的中文语音合成工具箱，包含语音编码器、语音合成器、声码器和可视化模块。

ttskit Text To Speech Toolkit: 语音合成工具箱。 安装 pip install -U ttskit 注意 可能需另外安装的依赖包：torch，版本要求torch=1.6.0,=1.7.1，根据自己的实际环境安装合适cuda或cpu版本的torch。 ttskit的

KDD 483 Jan 04, 2023
Making text a first-class citizen in TensorFlow.

TensorFlow Text - Text processing in Tensorflow IMPORTANT: When installing TF Text with pip install, please note the version of TensorFlow you are run

1k Dec 26, 2022
An implementation of model parallel GPT-3-like models on GPUs, based on the DeepSpeed library. Designed to be able to train models in the hundreds of billions of parameters or larger.

GPT-NeoX An implementation of model parallel GPT-3-like models on GPUs, based on the DeepSpeed library. Designed to be able to train models in the hun

EleutherAI 3.1k Jan 08, 2023
초성 해석기 based on ko-BART

초성 해석기 개요 한국어 초성만으로 이루어진 문장을 입력하면, 완성된 문장을 예측하는 초성 해석기입니다. 초성: ㄴㄴ ㄴㄹ ㅈㅇㅎ 예측 문장: 나는 너를 좋아해 모델 모델은 SKT-AI에서 공개한 Ko-BART를 이용합니다. 데이터 문장 단위로 이루어진 아무 코퍼스나

Dawoon Jung 29 Oct 28, 2022
CCQA A New Web-Scale Question Answering Dataset for Model Pre-Training

CCQA: A New Web-Scale Question Answering Dataset for Model Pre-Training This is the official repository for the code and models of the paper CCQA: A N

Meta Research 29 Nov 30, 2022
Web mining module for Python, with tools for scraping, natural language processing, machine learning, network analysis and visualization.

Pattern Pattern is a web mining module for Python. It has tools for: Data Mining: web services (Google, Twitter, Wikipedia), web crawler, HTML DOM par

Computational Linguistics Research Group 8.4k Dec 30, 2022
Implementation of Memorizing Transformers (ICLR 2022), attention net augmented with indexing and retrieval of memories using approximate nearest neighbors, in Pytorch

Memorizing Transformers - Pytorch Implementation of Memorizing Transformers (ICLR 2022), attention net augmented with indexing and retrieval of memori

Phil Wang 364 Jan 06, 2023
Converts python code into c++ by using OpenAI CODEX.

🦾 codex_py2cpp 🤖 OpenAI Codex Python to C++ Code Generator Your Python Code is too slow? 🐌 You want to speed it up but forgot how to code in C++? ⌨

Alexander 423 Jan 01, 2023
SimCTG - A Contrastive Framework for Neural Text Generation

A Contrastive Framework for Neural Text Generation Authors: Yixuan Su, Tian Lan,

Yixuan Su 345 Jan 03, 2023
A Structured Self-attentive Sentence Embedding

Structured Self-attentive sentence embeddings Implementation for the paper A Structured Self-Attentive Sentence Embedding, which was published in ICLR

Kaushal Shetty 488 Nov 28, 2022
Google and Stanford University released a new pre-trained model called ELECTRA

Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants. For furth

Yiming Cui 1.2k Dec 30, 2022
📜 GPT-2 Rhyming Limerick and Haiku models using data augmentation

Well-formed Limericks and Haikus with GPT2 📜 GPT-2 Rhyming Limerick and Haiku models using data augmentation In collaboration with Matthew Korahais &

Bardia Shahrestani 2 May 26, 2022
A python wrapper around the ZPar parser for English.

NOTE This project is no longer under active development since there are now really nice pure Python parsers such as Stanza and Spacy. The repository w

ETS 49 Sep 12, 2022
HAIS_2GNN: 3D Visual Grounding with Graph and Attention

HAIS_2GNN: 3D Visual Grounding with Graph and Attention This repository is for the HAIS_2GNN research project. Tao Gu, Yue Chen Introduction The motiv

Yue Chen 1 Nov 26, 2022