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README.md
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splits:
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- name: train
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num_bytes: 152600214
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num_examples: 217
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- name: validation
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num_bytes: 19881207
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num_examples: 25
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- name: test
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num_bytes: 24789618
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num_examples: 24
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download_size: 57731247
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dataset_size: 197271039
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configs:
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- config_name: default
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data_files:
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- split: train
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path: data/train-*
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- split: validation
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path: data/validation-*
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- split: test
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path: data/test-*
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---
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language:
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- bo
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license: other
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task_categories:
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- token-classification
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pretty_name: Tibetan Annotation Layer Detection
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tags:
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- tibetan
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- openpecha
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- ner
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- quotation
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- span-detection
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size_categories:
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- n<1K
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source_datasets:
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- tsadra/OpenPecha
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---
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# Tibetan Annotation Layer Detection
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Book-level dataset for multi-layer span detection in classical Tibetan texts (OpenPecha / tsadra). Each row is one book: raw text + cleaned character-offset spans across seven modeled annotation layers.
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## Schema
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| Column | Type | Description |
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|---|---|---|
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| `book_id` | string | OpenPecha book id (e.g. `P000010`) |
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| `text` | string | Full book text (`base/v001.txt`) |
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| `spans` | list of `{{start, end, label}}` | Inclusive character offsets; label is one of `QUOTATION`, `SABCHE`, `TSAWA`, `YIGCHUNG`, `CHAPTER`, `AUTHOR`, `BOOKTITLE` |
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| `in_scope_layers` | list of string | Layers this book is in-scope for (absent spans are trustworthy negatives) |
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Splits: `train` / `validation` / `test` (book-level stratified split; never split inside a book).
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## Data quality notes
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- **512 invalid spans dropped** (480 Quotation, 30 Chapter, 2 Yigchung) — all `inverted_empty_start_eq_end_plus_1`; none recoverable.
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- **Citation excluded** — only 2 books, 88% invalid.
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- **Author / BookTitle** are in model scope experimentally (~1 span/book); may be demoted to metadata-only if they underperform.
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- Spans are **not** pre-combined into BIO tags — overlapping spans are preserved at full fidelity; priority resolution happens at training time.
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