Datasets:

openpecha
/

tibetan-metadata-llm-sft-full

license: cc-by-4.0
task_categories:
  - text-generation
language:
  - bo
tags:
  - tibetan
  - metadata
  - title
  - author
  - llm-sft
  - tilamb
size_categories:
  - 100K<n<1M

Tibetan metadata LLM SFT dataset (full)

Complete supervised fine-tuning JSONL for title and author span extraction from BDRC outliner segments, built for TiLamb-7B.

Pilot subset

See ganga4364/tibetan-metadata-llm-sft for a 10% pilot subsample (same schema, smaller for quick experiments).

Layout

title/{train,val,test}.jsonl       # Alpaca format for LLaMA-Factory
title/{train,val,test}_meta.jsonl
author/{train,val,test}.jsonl
author/{train,val,test}_meta.jsonl
dataset_info.json                  # LLaMA-Factory registry snippet
reports/crop_stats.json

Each training row:

instruction — fixed task prompt (title or author)
input — cropped segment text (token-budget ≤3584 via TiLamb tokenizer)
output — JSON {"spans":[{"text","start","end"}]} (crop-relative offsets)

Cropping

Kind	When
`full`	Whole segment fits token budget
`positive`	Random window containing gold span (anti position-bias)
`negative`	Random window, empty spans for that task

Tokenizer budgets use TiLamb (YoLo2000/TiLamb-7B) — enforced in tokens, not characters.

Source: ganga4364/tibetan-metadata-extracted (3794 docs).

Built with OpenPecha/tibetan-text-meta-detection llm_sft package.