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README.md
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---
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license: mit
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task_categories:
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- text-classification
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- text-retrieval
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language:
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- en
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- fr
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tags:
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- grants
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- tenders
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- multilingual
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- africa
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- govtech
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- information-retrieval
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- synthetic
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pretty_name: "T2.2 Multilingual Grant & Tender Dataset"
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size_categories:
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- n<1K
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---
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# T2.2 — Multilingual Grant & Tender Dataset
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Synthetic dataset for the AIMS KTT Fellowship Hackathon, Challenge T2.2:
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**Multilingual Grant & Tender Matcher with Summarizer**.
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## Dataset Description
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Reproducible synthetic dataset of African Union and regional grant/tender documents,
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designed to test multilingual information retrieval and summarization systems.
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### Files
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| File | Description |
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|------|-------------|
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| `tenders/*.txt` | 32 English tender documents (24 plain text + 8 HTML) |
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| `tenders/*.txt` | 8 French tender documents |
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| `profiles.json` | 10 business profiles (Rwanda, Kenya, Senegal, DRC, Ethiopia) |
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| `gold_matches.csv` | 30 expert-curated (profile, tender) gold match pairs |
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### Tender Fields
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Each tender document contains:
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- `TITLE` / `TITRE`
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- `SECTOR` / `SECTEUR` — one of: agritech, healthtech, cleantech, edtech, fintech, wastetech
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- `BUDGET` — one of: USD 5,000 / 50,000 / 200,000 / 1,000,000
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- `DEADLINE` / `DATE LIMITE` — ISO date
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- `REGION` / `RÉGION` — East/West/Central/Southern Africa or Pan-Africa
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- `ELIGIBILITY` / `ÉLIGIBILITÉ`
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- Boilerplate bureaucratic text
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### Profile Fields
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```json
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{
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"id": "01",
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"sector": "agritech",
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"country": "Rwanda",
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"employees": 5,
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"languages": ["en"],
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"needs_text": "We need funding to scale our drone-based crop monitoring service...",
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"past_funding": "World Bank small grant 2023"
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}
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```
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## Generation
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Fully reproducible:
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```bash
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git clone https://github.com/YOUR_USERNAME/ktt-hackathon-multilingual
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cd ktt-hackathon-multilingual
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pip install -r requirements.txt
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python generate_data.py # regenerates all files with seed=42
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```
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## Usage
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```bash
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python matcher.py --profile 02 --topk 5
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```
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## License
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MIT
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