# DKSplit – Fast Word Segmentation for Python > Source: > Published: 2026-07-23 22:08:18+00:00 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. ``` python 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']] ``` 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. — one answer per input; pipelines, aggregation, statistics.`split()` — 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%).`split_topk()` 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. 1,000 hand-audited domain prefixes drawn from the [Newly Registered Domains Database (NRDS)](https://domainkits.com/download/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](https://github.com/ABTdomain/dksplit/tree/main/benchmark) directory: `sample_1000.csv` and `benchmark_5000.csv` , a larger set built the same way (also on Hugging Face as [ABTdomain/dksplit-benchmark](https://huggingface.co/datasets/ABTdomain/dksplit-benchmark)). To explore domain data yourself, register at [domainkits.com](https://domainkits.com)— fresh .com NRD downloads are free. | 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% | ``` 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. | 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 | 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](https://abtdomain.com/blog/2026/04/dksplit-update-cleaner-benchmark-first-deberta-run-different-failure-modes/). **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 **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. - Read more about DKSplit: [DKSplit on EuroHPC](https://abtdomain.com/blog/tag/eurohpc) - Website: [domainkits.com](https://domainkits.com),[abtdomain.com](https://abtdomain.com) - PyPI: [pypi.org/project/dksplit](https://pypi.org/project/dksplit) - Hugging Face (LLM variant): [ABTdomain/dksplit-qwen-lora](https://huggingface.co/ABTdomain/dksplit-qwen-lora) - Issues: [GitHub Issues](https://github.com/ABTdomain/dksplit/issues) [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Attribution required: credit "DKSplit by [ABTdomain](https://abtdomain.com)" in your README, documentation, about page, or API response metadata. The model was trained on the [Leonardo Booster](https://www.hpc.cineca.it/systems/hardware/leonardo/) supercomputer at CINECA, Italy, with computing resources provided by the [EuroHPC Joint Undertaking](https://eurohpc-ju.europa.eu/) through the Playground Access program (EHPC-AIF-2026PG01-281). We thank EuroHPC JU for enabling SMEs to explore new possibilities with world-class HPC infrastructure.