library_name: bibr
base_model: sentence-transformers/all-MiniLM-L6-v2
language: en
tags:
- text-classification
- scholarly-documents
bibr section classifier
Two-head classifier that bibr uses to type section headings it cannot resolve with its alias lookup. It predicts one of 16 canonical section types and whether the heading is top level. Encoder: all-MiniLM-L6-v2 (22M parameters), mean pooling, input template v3 (heading, position bucket, neighbouring headings, start of the body).
This revision retrains the same architecture and template on a larger, re-labelled corpus. It is a drop-in replacement for the September 2026 release: same files, same label order, same runtime cost.
Training data
section_v5b_leakfiltered: section headings from PMC Open Access JATS papers (English), paper-grouped train/val/test split. Three papers removed by a later leak re-check are excluded.- Train subsample of at most 3,000 rows per class: 45,979 rows from 6,946 papers. Epoch chosen on the v5b validation split only.
- No bibr-validation gold, blind evaluation, or held-out benchmark paper is in the training or validation data (matched by DOI, PMCID and title).
Evaluation
Canonical-type macro-F1 / accuracy (model-routed rows: macro-F1). "Model-routed" is the subset bibr actually sends to this model (headings the alias lookup misses), scored on bibr's section gold.
| set | previous release | this revision |
|---|---|---|
| v5b test (English JATS, 9,507 headings) | 0.589 / 0.398 | 0.966 / 0.946 |
| gold, English headings (9,501) | 0.656 / 0.572 | 0.798 / 0.784 |
| gold, non-English headings (1,524) | 0.213 / 0.295 | 0.265 / 0.344 |
| gold, model-routed English (3,472) | 0.419 | 0.676 |
| gold, model-routed non-English (1,240) | 0.168 | 0.217 |
Part of the gain on gold comes from shared labelling conventions: the training data and the gold both label subsections by their parent type and structured-abstract parts as abstract.
Non-English headings are zero-shot: the training data is English only.
Files
model.safetensors, tokenizer.json, tokenizer_config.json,
label_classes.json, inference_config.json (template version 3), and an
ONNX Runtime bundle in onnx/ used by bibr's core install.