mygithub.libinneed.workers.dev/sugarme/tokenizer

Tokenizer

Overview

tokenizer is pure Go package to facilitate applying Natural Language Processing (NLP) models train/test and inference in Go.

It is heavily inspired by and based on the popular HuggingFace Tokenizers.

tokenizer is part of an ambitious goal (together with transformer and gotch) to bring more AI/deep-learning tools to Gophers so that they can stick to the language they love and build faster software in production.

Features

tokenizer is built in modules located in sub-packages.

• Normalizer
• Pretokenizer
• Tokenizer
• Post-processing

It implements various tokenizer models:

• Word level model
• Wordpiece model
• Byte Pair Encoding (BPE)

It can be used for both training new models from scratch or fine-tuning existing models. See examples detail.

Basic example

This tokenizer package is compatible to load pretrained models from Huggingface. Some of them can be loaded using pretrained subpackage.

import (
	"fmt"
	"log"

	"github.com/sugarme/tokenizer/pretrained"
)

func main() {
    // Download and cache pretrained tokenizer. In this case `bert-base-uncased` from Huggingface
    // can be any model with `tokenizer.json` available. E.g. `tiiuae/falcon-7b`
	configFile, err := tokenizer.CachedPath("bert-base-uncased", "tokenizer.json")
	if err != nil {
		panic(err)
	}

	tk, err := pretrained.FromFile(configFile)
	if err != nil {
		panic(err)
	}

	sentence := `The Gophers craft code using [MASK] language.`
	en, err := tk.EncodeSingle(sentence)
	if err != nil {
		log.Fatal(err)
	}

	fmt.Printf("tokens: %q\n", en.Tokens)
	fmt.Printf("offsets: %v\n", en.Offsets)

	// Output
	// tokens: ["the" "go" "##pher" "##s" "craft" "code" "using" "[MASK]" "language" "."]
	// offsets: [[0 3] [4 6] [6 10] [10 11] [12 17] [18 22] [23 28] [29 35] [36 44] [44 45]]
}

All models can be loaded from files manually. pkg.go.dev for detail APIs.

Getting Started

• See pkg.go.dev for detail APIs

License

tokenizer is Apache 2.0 licensed.

Acknowledgement

• This project has been inspired and used many concepts from HuggingFace Tokenizers.