dksplit 1.0.2

High-performance string segmentation using BiLSTM-CRF

DKSplit

Fast character-level segmentation for web-style concatenated strings — domain names, hashtags, usernames, slugs. 9 MB ONNX model, CPU-only.

pip install dksplit

Requires Python >= 3.8. Dependencies: numpy, onnxruntime.

Usage

import dksplit

# Single best segmentation
dksplit.split("kubernetescluster")
# ['kubernetes', 'cluster']

# Batch (faster for large volumes; results identical to split())
dksplit.split_batch(["openaikey", "microsoftoffice", "bitcoinprice"])
# [['openai', 'key'], ['microsoft', 'office'], ['bitcoin', 'price']]

# Ranked candidates for ambiguous inputs
dksplit.split3("noranite")        # top-3, best first
# [['nora', 'nite'], ['noranite'], ['nor', 'anite']]

dksplit.split5("pikahug")         # top-5
# [['pikahug'], ['pika', 'hug'], ['pik', 'ahug'], ['pikah', 'ug'], ['pi', 'kahug']]

dksplit.split_topk("chatgptlogin", k=3)   # any k
# [['chatgpt', 'login'], ['chatgptlogin'], ['chatgpt', 'log', 'in']]

What can you do with it

Typical uses: spotting brands and lookalikes in newly registered domains (yourbrandlogin, getyourbrand), extracting keywords from domains, hashtags, and URLs, normalizing concatenated identifiers before matching and dedup, understanding spaceless search queries.

• split() — one answer per input; pipelines, aggregation, statistics.
• split_topk() — ranked candidates for recall-sensitive matching or for reranking with your own signals (brand lists, frequency data); an acceptable segmentation is in the top-3 candidates 98.5% of the time (top-5: 99.3%).

What's New in v1.0.2

Bugfix: split_batch() could differ from split() on rare inputs; results are now guaranteed identical. Pass exact=False to keep the old ~2x faster behavior.

Benchmark

Dataset

1,000 hand-audited domain prefixes drawn from the Newly Registered Domains Database (NRDS) (.com feed). No filtering or cherry-picking on segmentation difficulty. Ground truth was established through multi-model cross-validation (BiLSTM, Qwen 9B LoRA, Gemma 31B) and human audit. Each row provides a primary truth and an optional might_right field for genuinely ambiguous cases (e.g. brand-versus-compound).

Both benchmark sets ship in this repo's /benchmark directory: sample_1000.csv and benchmark_5000.csv, a larger set built the same way (also on Hugging Face as ABTdomain/dksplit-benchmark). To explore domain data yourself, register at domainkits.com — fresh .com NRD downloads are free.

Results

Model	Strict EM	Lenient EM
DKSplit v1.0.2	86.5%	91.5%
WordSegment	65.2%	69.5%
WordNinja	51.0%	54.0%

Strict EM counts only exact matches against truth. Lenient EM also accepts the might_right alternative when present.

Top-k coverage (an acceptable segmentation is present within the candidates):

Benchmark	top-1	top-3	top-5
1,000 samples	91.5%	98.5%	99.3%
5,000 samples	90.4%	97.8%	99.0%

Reproduce it yourself

git clone https://github.com/ABTdomain/dksplit.git
cd dksplit/benchmark
pip install dksplit wordsegment wordninja
python run_benchmark.py                     # 1,000-sample set
python run_benchmark.py benchmark_5000.csv  # 5,000-sample set

Adding your own segmenter to the comparison is a one-line change in run_benchmark.py. Pull requests for ambiguous samples are welcome.

Comparison

Input	DKSplit v1.0.2	WordSegment	WordNinja
chatgptprompts	chatgpt prompts	chat gpt prompts	chat gp t prompts
spotifywrapped	spotify wrapped	spot if y wrapped	spot if y wrapped
ethereumwallet	ethereum wallet	e there um wallet	e there um wallet
kubernetescluster	kubernetes cluster	ku bernet es cluster	ku berne tes cluster
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

How It Works

DKSplit treats segmentation as a character-level sequence labeling task. The training data includes LLM-labeled domain segmentations, brand names, personal name combinations, multilingual phrases (English, French, German, Spanish, and more), and tech product names. At inference, the BiLSTM runs as an INT8-quantized ONNX model and CRF decoding is performed in NumPy. No GPU required; around 800 samples per second on a single CPU thread.

Why BiLSTM-CRF: character precision, CPU-only inference, a 9 MB artifact — built for millions of strings per day. Design rationale and failure-mode comparisons (dictionary segmenters, DeBERTa-V3, LLMs): blog post.

Features

• Brand-aware: recognizes thousands of brands, tech products, and proper nouns
• Multilingual: English, French, German, Spanish, and romanized text
• Lightweight: 9 MB model, minimal dependencies (numpy + onnxruntime)
• Offline: no API keys, no internet required
• Top-k candidates: split3 / split5 / split_topk return ranked alternative segmentations

Limitations

• Characters: a-z and 0-9, auto-lowercased. For best results pass letter-only runs: split off digits and separators (-, ., _) with simple rules first — those boundaries are a job for rules, not the model.
• Max length: 64 characters.
• Script: Latin script only. Non-Latin scripts (汉字, かな, 한글, العربية) are not supported.
• Ambiguity: some inputs are genuinely ambiguous. split() optimizes for the most common interpretation; use split_topk() when you need the alternatives.
• Rare languages: accuracy is highest on English and major European languages.

Links

• Website: domainkits.com, ABTdomain.com
• PyPI: pypi.org/project/dksplit
• Hugging Face (LLM variant): ABTdomain/dksplit-qwen-lora
• Issues: GitHub Issues

License

CC BY 4.0. Attribution required: credit "DKSplit by ABTdomain" in your README, documentation, about page, or API response metadata.

Acknowledgements

 

The 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.