DKSplit-go

v0.3.1 — Model upgraded to EuroHPC infrastructure (Leonardo Booster, NVIDIA A100). ~3% accuracy improvement over v0.2.x on real-world domains.

Go implementation of DKSplit - fast word segmentation for text without spaces.

Built with BiLSTM-CRF model (9.47M parameters) and ONNX Runtime. The Go and Python versions use the same model and produce identical results.

Install

go get github.com/ABTdomain/dksplit-go

Usage

package main

import (
    "fmt"
    "log"

    dksplit "github.com/ABTdomain/dksplit-go"
)

func main() {
    splitter, err := dksplit.New("models")
    if err != nil {
        log.Fatal(err)
    }
    defer splitter.Close()

    // Single
    result, _ := splitter.Split("chatgptlogin")
    fmt.Println(result)
    // Output: [chatgpt login]

    // Batch
    results, _ := splitter.SplitBatch([]string{"openaikey", "microsoftoffice"}, 256)
    fmt.Println(results)
    // Output: [[openai key] [microsoft office]]
}

Performance

CPU	Mode	QPS
Intel Core i9-14900K	Single	~1,700/s
Intel Core i9-14900K	Batch	~7,000/s
Intel Core i9-9900K	Single	~1,000/s
Intel Core i9-9900K	Batch	~3,000/s

Batch mode is 4.6x faster than single mode.

Compared to Python version:

• Single: 2.7x faster
• Batch: 5.6x faster

Benchmark

Dataset

1,000 domains randomly sampled from the Newly Registered Domains Database (NRDS) (.com feed, April 8, 2026). No filtering or cherry-picking. Ground truth was established through multi-model cross-validation (BiLSTM, Qwen 9B LoRA, Gemma 31B) and human audit.

The dataset and evaluation script are available on GitHub.

Results

Accuracy on 1,000 randomly sampled real-world .com domains, human-audited ground truth:

Model	Accuracy
DKSplit v0.3.1	85.0%
DKSplit v0.2.x	82.8%
WordSegment	54.0%
WordNinja	46.1%

Note: The accuracy above is measured against a single reference segmentation. Domain names are inherently ambiguous. For example, tiantian5 could be tiantian 5 (Chinese compound name) or tian tian 5 (two separate syllables); noranite could be nora nite or an intact brand; pikahug could be pika hug or an intact brand name. Our audit found ~5% of test samples have multiple valid segmentations. Accounting for these, effective accuracy is closer to 90%.

Comparison

Input	DKSplit v0.3.1	WordSegment	WordNinja
chatgptprompts	chatgpt prompts	chat gpt prompts	chat gp t prompts
tensorflowserving	tensorflow serving	tensor flow serving	tensor flow serving
spotifywrapped	spotify wrapped	spot if y wrapped	spot if y wrapped
ethereumwallet	ethereum wallet	e there um wallet	e there um wallet
cloudflarecdn	cloudflare cdn	cloud flare cdn	cloud flare cd n
kubernetescluster	kubernetes cluster	ku bernet es cluster	ku berne tes cluster
hackathonwinners	hackathon winners	hackathon winners	hack a th on winners
whatsappstatus	whatsapp status	what sapp status	what s app status
drwatsonai	dr watson ai	dr watson a i	dr watson a i
escribirenvozalta	escribir en voz alta	escribir env oz alta	es crib ire nv oz alta
tuvasou	tu vas ou	tuva sou	tuva so u
candidiasenuncamais	candidiase nunca mais	candid iase nunca mais	can didi as e nun cama is
robertdeniro	robert de niro	robert deniro	robert deniro
mercibeaucoup	merci beaucoup	merci beaucoup	mer ci beau coup

Features

• Brand-aware: Recognizes thousands of brands, tech products, and proper nouns
• Multilingual: Handles English, French, German, Spanish, and romanized text
• Lightweight: 9 MB model, ONNX Runtime inference
• Offline: No API keys, no internet required

Limitations

• Characters: Only a-z and 0-9. Input is automatically lowercased.
• Max length: 64 characters.
• Script: Latin script only.
• Platform: Linux x64 (ONNX Runtime shared library included).

Requirements

• Go 1.21+
• Linux x64

Links

• Website: domainkits.com, ABTdomain.com
• Python version: github.com/ABTdomain/dksplit
• PyPI: pypi.org/project/dksplit
• Hugging Face: huggingface.co/ABTdomain/dksplit

License

This project is licensed under the Apache License 2.0.

Please attribute as: DKsplit by ABTdomain

Acknowledgements

 

The v0.3.1 model was trained on the Leonardo Booster supercomputer at CINECA, Italy, with computing resources provided by the EuroHPC Joint Undertaking through the Playground Access program (project AIFAC_P02_281). We thank EuroHPC JU for enabling SMEs to explore new possibilities with world-class HPC infrastructure.