base on 💥 Fast State-of-the-Art Tokenizers optimized for Research and Production <p align="center"> <br> <img src="https://huggingface.co/landing/assets/tokenizers/tokenizers-logo.png" width="600"/> <br> <p> <p align="center"> <img alt="Build" src="https://github.com/huggingface/tokenizers/workflows/Rust/badge.svg"> <a href="https://github.com/huggingface/tokenizers/blob/main/LICENSE"> <img alt="GitHub" src="https://img.shields.io/github/license/huggingface/tokenizers.svg?color=blue&cachedrop"> </a> <a href="https://pepy.tech/project/tokenizers"> <img src="https://pepy.tech/badge/tokenizers/week" /> </a> </p> 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. ## Performances Performances can vary depending on hardware, but running the [~/bindings/python/benches/test_tiktoken.py](bindings/python/benches/test_tiktoken.py) should give the following on a g6 aws instance:  ## Bindings We provide bindings to the following languages (more to come!): - [Rust](https://github.com/huggingface/tokenizers/tree/main/tokenizers) (Original implementation) - [Python](https://github.com/huggingface/tokenizers/tree/main/bindings/python) - [Node.js](https://github.com/huggingface/tokenizers/tree/main/bindings/node) - [Ruby](https://github.com/ankane/tokenizers-ruby) (Contributed by @ankane, external repo) ## Installation You can install from source using: ```bash pip install git+https://github.com/huggingface/tokenizers.git#subdirectory=bindings/python ``` our install the released versions with ```bash pip install tokenizers ``` ## Quick example using Python: Choose your model between Byte-Pair Encoding, WordPiece or Unigram and instantiate a tokenizer: ```python 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: ```python 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: ```python 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: ```python output = tokenizer.encode("Hello, y'all! How are you 😁 ?") print(output.tokens) # ["Hello", ",", "y", "'", "all", "!", "How", "are", "you", "[UNK]", "?"] ``` Check the [documentation](https://huggingface.co/docs/tokenizers/index) or the [quicktour](https://huggingface.co/docs/tokenizers/quicktour) to learn more! ", Assign "at most 3 tags" to the expected json: {"id":"1996","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"