| --- |
| dataset_info: |
| features: |
| - name: text |
| dtype: string |
| - name: category |
| dtype: string |
| - name: extended_answer |
| sequence: |
| - name: user |
| dtype: string |
| - name: answer |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 363151819 |
| num_examples: 54348 |
| download_size: 136276329 |
| dataset_size: 363151819 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| --- |
| # DATASET: **Kazakh administrative documents for RAG document QA.** |
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| * **Structure.** Each item is a JSON object with: |
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| * `text`: the full Kazakh document body (biography or power-of-attorney). |
| * `category`: document type label — e.g., **Өмірбаян** (autobiographical CV/biography) and **Сенімхат** (power of attorney) etc. In Kazakh admin usage, *Өмірбаян* is a concise, chronological personal record; *Сенімхат* is a written authorization to act on someone’s behalf. |
| * `extended_answer`: list of `{user, answer}` QA pairs extractable from `text` (factoid fields like birth date/place, degrees, awards; or principals/children, validity period, addresses, etc.). |
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| * **Scope.** Kazakh-language **administrative and personal records** with explicit slot-like facts (names, dates, institutions, addresses, phone numbers) and templated legal phrasing (e.g., “сенімхат … жарамды”, placeholders like `[күні]`). The two shown categories align with common Kazakh document genres: autobiographies for employment/education workflows and powers of attorney for representation/transport of minors. |
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| * **Usage.** |
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| * **Extraction & slot filling:** train/evaluate NER/IE for structured fields (person, DOB, place, degree, positions; principal/agent, children, validity window). |
| * **Document QA / RAG:** `extended_answer` provides supervision for extractive/generative QA grounded in `text`. |
| * **Template validation & completion:** detect missing placeholders (e.g., `[күні]`) and verify mandatory fields typical for *сенімхат*; learn document-type–specific consistency rules. |
| * **Document classification:** use `category` to train classifiers distinguishing biography vs. authorization letters. |
| * **Privacy stress-tests:** includes realistic PII (addresses, phone numbers) to test redaction or safe-answering policies (if needed). |
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| **Notes.** No cross-file alignment is required; each JSON is self-contained: raw text + gold QA pairs enable end-to-end pipelines (parse → retrieve within doc → answer). The dataset is suitable for low-resource Kazakh NLP where domain conventions for *өмірбаян* and *сенімхат* are well-defined. |
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