Awesome

Tokenizers

Go bindings for the HuggingFace Tokenizers library.

Installation

make build to build libtokenizers.a that you need to run your application that uses bindings. In addition, you need to inform the linker where to find that static library: go run -ldflags="-extldflags '-L./path/to/libtokenizers/directory'" . or just add it to the CGO_LDFLAGS environment variable: CGO_LDFLAGS="-L./path/to/libtokenizers/directory" to avoid specifying it every time.

Using pre-built binaries

If you don't want to install Rust toolchain, build it in docker: docker build --platform=linux/amd64 -f release/Dockerfile . or use prebuilt binaries from the releases page. Prebuilt libraries are available for:

• darwin-arm64
• linux-arm64
• linux-amd64

Getting started

TLDR: working example.

Load a tokenizer from a JSON config:

import "github.com/daulet/tokenizers"

tk, err := tokenizers.FromFile("./data/bert-base-uncased.json")
if err != nil {
    return err
}
// release native resources
defer tk.Close()

Load a tokenizer from Huggingface:

import "github.com/daulet/tokenizers"

tk, err := tokenizers.FromPretrained("google-bert/bert-base-uncased")
if err != nil {
    return err
}
// release native resources
defer tk.Close()

Encode text and decode tokens:

fmt.Println("Vocab size:", tk.VocabSize())
// Vocab size: 30522
fmt.Println(tk.Encode("brown fox jumps over the lazy dog", false))
// [2829 4419 14523 2058 1996 13971 3899] [brown fox jumps over the lazy dog]
fmt.Println(tk.Encode("brown fox jumps over the lazy dog", true))
// [101 2829 4419 14523 2058 1996 13971 3899 102] [[CLS] brown fox jumps over the lazy dog [SEP]]
fmt.Println(tk.Decode([]uint32{2829, 4419, 14523, 2058, 1996, 13971, 3899}, true))
// brown fox jumps over the lazy dog

Encode text with options:

var encodeOptions []tokenizers.EncodeOption
encodeOptions = append(encodeOptions, tokenizers.WithReturnTypeIDs())
encodeOptions = append(encodeOptions, tokenizers.WithReturnAttentionMask())
encodeOptions = append(encodeOptions, tokenizers.WithReturnTokens())
encodeOptions = append(encodeOptions, tokenizers.WithReturnOffsets())
encodeOptions = append(encodeOptions, tokenizers.WithReturnSpecialTokensMask())

// Or just basically
// encodeOptions = append(encodeOptions, tokenizers.WithReturnAllAttributes())

encodingResponse := tk.EncodeWithOptions("brown fox jumps over the lazy dog", false, encodeOptions...)
fmt.Println(encodingResponse.IDs)
// [2829 4419 14523 2058 1996 13971 3899]
fmt.Println(encodingResponse.TypeIDs)
// [0 0 0 0 0 0 0]
fmt.Println(encodingResponse.SpecialTokensMask)
// [0 0 0 0 0 0 0]
fmt.Println(encodingResponse.AttentionMask)
// [1 1 1 1 1 1 1]
fmt.Println(encodingResponse.Tokens)
// [brown fox jumps over the lazy dog]
fmt.Println(encodingResponse.Offsets)
// [[0 5] [6 9] [10 15] [16 20] [21 24] [25 29] [30 33]]

Benchmarks

go test . -run=^\$ -bench=. -benchmem -count=10 > test/benchmark/$(git rev-parse HEAD).txt

Decoding overhead (due to CGO and extra allocations) is between 2% to 9% depending on the benchmark.

go test . -bench=. -benchmem -benchtime=10s

goos: darwin
goarch: arm64
pkg: github.com/daulet/tokenizers
BenchmarkEncodeNTimes-10     	  959494	     12622 ns/op	     232 B/op	      12 allocs/op
BenchmarkEncodeNChars-10      1000000000	     2.046 ns/op	       0 B/op	       0 allocs/op
BenchmarkDecodeNTimes-10     	 2758072	      4345 ns/op	      96 B/op	       3 allocs/op
BenchmarkDecodeNTokens-10    	18689725	     648.5 ns/op	       7 B/op	       0 allocs/op
PASS
ok   github.com/daulet/tokenizers 126.681s

Run equivalent Rust tests with cargo bench.

decode_n_times          time:   [3.9812 µs 3.9874 µs 3.9939 µs]
                        change: [-0.4103% -0.1338% +0.1275%] (p = 0.33 > 0.05)
                        No change in performance detected.
Found 7 outliers among 100 measurements (7.00%)
  7 (7.00%) high mild

decode_n_tokens         time:   [651.72 ns 661.73 ns 675.78 ns]
                        change: [+0.3504% +2.0016% +3.5507%] (p = 0.01 < 0.05)
                        Change within noise threshold.
Found 7 outliers among 100 measurements (7.00%)
  2 (2.00%) high mild
  5 (5.00%) high severe

Contributing

Please refer to CONTRIBUTING.md for information on how to contribute a PR to this project.