---
language:
- bo
license: other
task_categories:
- token-classification
pretty_name: Tibetan Annotation Layer Detection
tags:
- tibetan
- openpecha
- tsadra
- ner
- quotation
- span-detection
- buddhist-studies
size_categories:
- n<1K
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
- config_name: windowed_w8192_s4916
  data_files:
  - split: train
    path: windowed_w8192_s4916/train/*.parquet
  - split: validation
    path: windowed_w8192_s4916/validation/*.parquet
  - split: test
    path: windowed_w8192_s4916/test/*.parquet
---

# Tibetan Annotation Layer Detection

266 annotated Classical Tibetan books with seven annotation layers — Quotation,
Sabche, Tsawa, Yigchung, Chapter, Author, BookTitle — stored as character-offset
spans over a flat base text. Each row is one book: `book_id`, `text`, `spans`
(`{start, end, label}`), and `in_scope_layers`.

Book-level split: **217 train / 25 validation / 24 test**. A book is never split
across partitions.

This revision (`v1.1`) is a span-level clean of `v1.0`. Texts, book membership,
split assignment, and split order are unchanged.

## What changed in v1.1

Every span with `start == end` was dropped, on all seven layers. No other spans
were removed. Impossible geometries (`start > end`, negative offsets, offsets
beyond the book text) were scanned and **none were found**.

Character mass uses inclusive length `end - start + 1` (see caveat below).

| Layer | Spans before | Spans after | Removed | Char mass before | Char mass after |
|---|---:|---:|---:|---:|---:|
| Quotation | 24,730 | 24,722 | 8 | 3,415,105 | 3,415,097 |
| Sabche | 18,847 | 18,845 | 2 | 1,608,291 | 1,608,289 |
| Tsawa | 6,845 | 6,811 | 34 | 1,285,298 | 1,285,264 |
| Yigchung | 9,025 | 8,804 | 221 | 841,535 | 841,314 |
| Chapter | 1,637 | 1,637 | 0 | 94,724 | 94,724 |
| Author | 311 | 311 | 0 | 12,160 | 12,160 |
| BookTitle | 266 | 266 | 0 | 21,938 | 21,938 |
| **Total** | **61,661** | **61,396** | **265** | **7,279,051** | **7,278,786** |

All 265 removals were `start == end`. 43 books lost at least one span:

| Book | Spans removed |
|---|---:|
| P000036 | 58 |
| P000140 | 33 |
| P000236 | 21 |
| P000275 | 20 |
| P000087 | 16 |
| P000123 | 14 |
| P000153 | 14 |
| P000195 | 8 |
| P000138 | 6 |
| P000073 | 5 |
| P000132 | 5 |
| P000174 | 4 |
| P000030 | 4 |
| P000034 | 3 |
| P000037 | 3 |
| P000054 | 3 |
| P000089 | 3 |
| P000119 | 3 |
| P000130 | 3 |
| P000152 | 3 |
| P000159 | 3 |
| P000010 | 3 |
| P000011 | 3 |
| P000171 | 3 |
| P000218 | 3 |
| P000055 | 2 |
| P000199 | 2 |
| P000230 | 2 |
| P000021 | 1 |
| P000028 | 1 |
| P000050 | 1 |
| P000051 | 1 |
| P000096 | 1 |
| P000126 | 1 |
| P000156 | 1 |
| P000175 | 1 |
| P000203 | 1 |
| P000207 | 1 |
| P000216 | 1 |
| P000224 | 1 |
| P000258 | 1 |
| P000151 | 1 |
| P000246 | 1 |

`v1.0` remains reachable as the `v1.0` tag (commit `aaa17607`).

## Why

`start == end` spans are zero-length or one-character stray marks, not real
annotations. The previous validator only checked geometric coherence (negative,
inverted, out of bounds), so these passed silently.

## Flagged books (zero spans for an in-scope layer)

These books are **retained**. Their in-scope layer with no remaining spans is
unresolved — they are not dropped from the split.

- **P000218** (test): 847,725 characters; had 3 Quotation spans, all `start == end`.
  After this clean it has **no Quotation annotation at all**, while still marked
  in-scope for Quotation.
- **P000126** (train): already had **zero** Quotation spans while marked in-scope.
  Unchanged by this clean (one non-Quotation `start == end` span was dropped).

No other in-scope layer on any book went to zero spans as a result of this clean.

## Known remaining issues (not fixed here)

- Some books have very low annotation density relative to their length.
- Some books have large positional gaps with no annotation.
- Damage has only been systematically examined for **Quotation**. The other six
  layers were cleaned of `start == end` here but have not been inspected for
  density or gaps.
- No minimum-length rule, density filter, or gap detection was applied.

The offset convention (inclusive vs half-open) has not been independently verified.

## License

Packaging, split, and this clean are released under CC0 1.0. Underlying texts and
original annotations come from OpenPecha / Tsadra; per-book source licenses vary.

<!-- windowed-v2-card -->
# Config `windowed_w8192_s4916` (tag v2.0)


# WARNING — derived data

This dataset is **derived** from a specific tokenizer, window length, and
stride. **Regenerate it** if any of those change. Do not mix windows from
different tokenizers or strides in the same training run.

Load the named config, never the unpinned default (that is still book-level):

```python
from datasets import load_dataset
ds = load_dataset("karma689/layer_detection", "windowed_w8192_s4916", revision="v2.0")
```

`revision="v1.1"` (default config) stays book texts + character spans.
Tag `v1.1` is not moved by this publish.

## Schedule warning

v1.3 trains on **14,338** windows/epoch (HF stride 5120, step 3072, 62.5%
overlap, `dataset_revision: null`). This config is step 4916 (~40% overlap).
The continuous extras-scale estimate was **~9,060** windows/epoch. The
**measured** HF overflow total is **8,906** (217+25+24 books → 6877+897+1132).
That 8,906 is the per-book tokenizer count, **not** the naive
`14338 × 3072/4916` formula and **not** 9,103 (false 1843-step model).

Relative to 14,338 this is ~38% fewer windows. **Warmup and the LR schedule
must be rescaled** or the same nominal epoch count undertrains.

## Provenance

| Field | Value |
|---|---|
| Source dataset | `karma689/layer_detection` |
| Source revision | `v1.1` |
| Source tag commit | `b9576f90b675240a6c59d5d0768afc89735a4bf3` |
| Tokenizer repo (files used) | `karma689/mmbert-base-layer-detection-v1.3` |
| Upstream tokenizer | `jhu-clsp/mmBERT-base` |
| Vocab size | 256000 |
| tokenizer.is_fast | True |
| Combined tokenizer-file sha256 | `d582312d8c4ccd73ba5a5199026bd161a869503946e488c5e79deb569150469f` |
| Window length (`max_length`) | 8192 |
| HF tokenizer `stride` (overlap) | 3276 |
| Step (CLI `--stride`) | 4916 |
| Label scheme | 15-class BIO softmax |
| Gold spans split across windows | **0** |
| Damaged books (O-mask on `loss_mask`) | P000218, P000271, P000078, P000126 |
| Measured max span | 3095 tokens — P000151 QUOTATION `[304112, 308503]` |

## Tokenizer file hashes

| File | sha256 |
|---|---|
| `tokenizer.json` | `609d8f4c067cd3950f88594c5a802616cea245823836ef5848ee4fc40aab5b6f` |
| `tokenizer_config.json` | `14b147f2a4f939d9b12ab36e9633917040dd948fa78ce283b03402e4cf2c9cba` |

Hub name `s4916` is the **step**, not the HuggingFace `stride` argument.
`tokenizer(..., max_length=8192, stride=3276)` yields step 4916.

The longest gold span is 3,095 tokens. A span fits whole in at least one
window iff its length is ≤ overlap. Step 4,916 clears that floor with margin.

## Window counts

| Split | Books | Windows |
|---|---:|---:|
| train | 217 | 6877 |
| validation | 25 | 897 |
| test | 24 | 1132 |
| **total** | **266** | **8906** |

Seventeen books are shorter than 8192 tokens; each produced exactly one window.
Shortest: P000052, 84 tokens.

`scope_mask` uses the same confirmed-scope policy as v1.3 training
(`apply_confirmed_scope`): P000010 excludes Quotation, P000100 excludes Tsawa.

Offset convention is **inclusive** `[start, end]` (`text[start:end+1]`).
Do not convert to half-open — that would change BIO and the v2 token indices.

BookTitle gold stays in `labels` as `B-BOOKTITLE` / `I-BOOKTITLE`. The same
positives v1.2/v1.3 ignore are zeroed on **`loss_mask` only**.

Overlapped tokens are labelled `-100` in every window except the first that
covers them, so each content token contributes to the BIO loss **once**.

## 15-label id → name (v1.3)

| id | label |
|---:|---|
| 0 | `O` |
| 1 | `B-QUOTE` |
| 2 | `I-QUOTE` |
| 3 | `B-SABCHE` |
| 4 | `I-SABCHE` |
| 5 | `B-TSAWA` |
| 6 | `I-TSAWA` |
| 7 | `B-YIGCHUNG` |
| 8 | `I-YIGCHUNG` |
| 9 | `B-CHAPTER` |
| 10 | `I-CHAPTER` |
| 11 | `B-AUTHOR` |
| 12 | `I-AUTHOR` |
| 13 | `B-BOOKTITLE` |
| 14 | `I-BOOKTITLE` |

## Columns

| Column | Description |
|---|---|
| `book_id` | OpenPecha id |
| `window_index` | 0-based window within the book |
| `input_ids` / `attention_mask` | mmBERT window (no padding) |
| `labels` | 15-class BIO, winner-takes-all (Quotation wins). Unmasked BookTitle. |
| `scope_mask` | Per-label in-scope mask |
| `offset_mapping` / `special_tokens_mask` | For char ↔ token maps |
| `spans` | Every overlapping gold span (not collapsed): Hub `layer`, half-open window token `start`/`end`, `truncated`, `span_id`, `char_start`/`char_end` |
| `start_targets` / `end_targets` | `[7, T]` float, MODEL_LAYERS order, smoothing 1.0/0.3/0.1 with `max()` |
| `loss_mask` | `[T]` float; 0 on specials/pad, BookTitle BIO tokens, and damaged-book tokens outside every gold span |

`span_id` is `{book_id}:{label}:{char_start}:{char_end}:{source_index}` and is
stable across windows that cover the same gold span.