| --- |
| license: apache-2.0 |
| language: |
| - en |
| library_name: onnx |
| base_model: BAAI/bge-small-en-v1.5 |
| pipeline_tag: text-classification |
| tags: |
| - business |
| - website-classification |
| - industry-classification |
| - operating-model |
| - requstory |
| --- |
| |
| # Requ Classify Small (preview) |
|
|
| **Reads a company's website and says what kind of business it is and which parts of the site describe real work.** |
| 33 million parameters, about 50 MB, runs on a laptop CPU. Made by [Requstory Labs](https://requstory.com/lab) in Manchester, UK. |
|
|
| This is the model behind the free company maps on [requstory.com](https://requstory.com): it gives each map an industry, |
| finds the offerings, teams and processes on the website, and picks the sentences that prove them. |
|
|
| > **Preview.** On 341 held-out websites it gets the industry right 52% of the time (top 3: 75%). Our release target is |
| > 65%. Use the `sure` flag: when the model is sure (36% of sites) it is right 75% of the time. |
|
|
| ## What it does |
|
|
| | Task | Output | Classes | |
| | --- | --- | --- | |
| | Industry | one industry with a probability, `sure`, and the top 3 | 64 industries, from accounting to water and waste | |
| | Item type | a type for each menu item, heading and button | offering, team, activity, audience, website, other | |
| | Page stage | the customer-journey stage of each page | discover, evaluate, buy, start, use, support, company | |
| | Process evidence | sentences that show a known business process of that industry | ~500 processes in 344 teams | |
|
|
| ## Use it |
|
|
| ```bash |
| pip install -r requirements.txt |
| python requ_classify.py < example.json |
| ``` |
|
|
| ```python |
| from requ_classify import RequClassify |
| model = RequClassify() # loads from this folder |
| result = model(site) # site = {"name", "description", "pages": [{"url", "title", "text", "items": [...]}]} |
| print(result["industry"]) # {"id": "dental", "p": 0.81, "sure": true, "top3": [...]} |
| ``` |
|
|
| Each page's `items` are `{"text", "kind"}` with kind one of `menu`, `footer`, `h1`, `h2`, `h3`, `button`. |
| Read a site's homepage and up to 5 inner pages; that is what the model was trained on. |
|
|
| ## How it works |
|
|
| 1. Text is embedded with [bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) (frozen, quantised ONNX). |
| 2. Small linear heads, trained on our labels, give the industry, item types and page stages (`heads.safetensors`). |
| 3. Process evidence: a gate keeps sentences that describe work, then matches them to the industry's processes in |
| `library.json`, using the library wording and similar sentences from training sites (`memory`). |
|
|
| ## Training data |
|
|
| - 2,536 business websites found through Wikidata and OpenStreetMap, read from their public pages |
| (homepage and up to 5 inner pages). |
| - Labels came from an open-weight teacher model (DeepSeek, MIT licence) reading the same pages, checked by hand on a sample. |
| - The industry and process library (64 industries, 344 teams, ~500 processes) was written for Requstory and reviewed by people. |
| - No page text is published in this package. `memory` holds sentence embeddings from training sites, not the sentences. |
|
|
| ## Results |
|
|
| On 341 held-out websites (`eval.json`): |
|
|
| | Measure | Score | |
| | --- | --- | |
| | Industry, top 1 | 52% | |
| | Industry, top 3 | 75% | |
| | Share of sites where the model is sure | 36% | |
| | Industry when sure | 75% | |
| | Item type | 68% | |
| | Page stage | 55% | |
| | Process match, top 1 | 41% | |
| | Before this model (rules) | 21% industry right | |
|
|
| ## Limits |
|
|
| - English only. Sites built entirely in JavaScript need a browser to read first. |
| - Known confusions: trades and personal services (a plumber labelled as beauty services), holding groups, marketplaces. |
| - It describes what a website shows, not how a company really works inside. |
| - Do not use it to make decisions about people, credit or employment. |
|
|
| ## Licence |
|
|
| Apache 2.0 for the heads, library and code. The bundled bge-small-en-v1.5 model is MIT (see `NOTICE`). |
|
|
| ## Cite |
|
|
| ``` |
| @misc{requstory2026requclassify, |
| title = {Requ Classify Small}, |
| author = {Requstory Labs}, |
| year = {2026}, |
| url = {https://huggingface.co/requstory/requ-classify-small} |
| } |
| ``` |
|
|
