Datasets:

ganga4364
/

tibetan-metadata-detector

doc_id stringlengths 36 36	filename stringlengths 19 48	segment_id stringlengths 36 36	segment_index int64 0 875	segment_label stringclasses 4 values	char_length int64 41 2.05M	token_length int64 31 1.46M	segment_tier stringclasses 3 values	has_title bool 2 classes	has_author bool 2 classes	window_annotations listlengths 0 2	window_name stringclasses 31 values	window_side stringclasses 3 values	slide_index int64 0 14	input_ids listlengths 512 512	attention_mask listlengths 512 512	labels listlengths 512 512	offset_mapping listlengths 512 512	label_list listlengths 5 5
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Tibetan Metadata Detector Dataset

RoBERTa sliding-window training data for Tibetan title and author span detection from BDRC outliner exports.

Contents (`windows` config)

Balanced window splits (fixed window-relative BIO labeling, O-only subsampling, author oversampling):

Split	Description
`train`	~89% of documents (stratified)
`validation`	~1% (small val for fast eval)
`test`	~10%

Balancing applied before split:

O-only windows capped at 2× entity-bearing windows per segment
Author-bearing windows duplicated 2×
Document-level stratified split 89% / 1% / 10%

Raw extracted documents: ganga4364/tibetan-metadata-extracted

Windowing (train = infer)

Tokenizer: spsither/tibetan_RoBERTa_S_e3
Window size: 512 subword tokens, stride 256
Short segments (≤512 tok): 1 window
Long segments: up to 15 begin + 15 end slides with overlap-aware deduplication

Fields

Each row:

input_ids, attention_mask, labels — HuggingFace-ready tensors (512 len)
offset_mapping — char offsets per token (segment-relative)
window_name, window_side, slide_index, window_annotations
doc_id, segment_id, segment_tier, has_title, has_author

Usage

from datasets import load_dataset

ds = load_dataset("ganga4364/tibetan-metadata-detector", "windows")
train = ds["train"]
val = ds["validation"]
test = ds["test"]

Train on a new GPU instance

pip install -r requirements.txt
python train_roberta.py \
  --hf-dataset ganga4364/tibetan-metadata-detector \
  --hf-config windows \
  --batch-size 64 \
  --epochs 3 \
  --entity-weight 10

Model & demo

Model: ganga4364/tibetan-metadata-roberta-ner
Demo: ganga4364/tibetan-metadata-highlight

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