Source: 3,794 extracted docs, Koichi tokenizer windows (934,690 raw → 222,320 balanced train)
3 epochs, batch size 64, lr 2e-5, entity weight 10
Sliding-window examples: 512 tokens, stride 256, 15B+15E overlap-aware
Split: 89% / 1% / 10%

Window-level test metrics (28,497 test windows)

Metric	Koichi	Spsither baseline
span F1	12.3%	3.1%
title F1	15.8%	7.4%
author F1	9.4%	1.0%

Inference

Use the same sliding-window pipeline as training (15 begin + 15 end, stride 256), merge overlapping predictions, then extract spans.

python

from pipeline.inference import load_model_and_tokenizer, predict_segment, highlight_spans

model, tokenizer, device = load_model_and_tokenizer("ganga4364/tibetan-metadata-koichi-ner")
spans = predict_segment(model, tokenizer, your_tibetan_text, device=device)
print(highlight_spans(your_tibetan_text, spans))

Demo

Try the Gradio Space: ganga4364/tibetan-metadata-highlight