README.md

metadata

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.

• Structure. Each item is a JSON object with:

  • 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.).

• 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.

• Usage.

  • 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).

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.