---
license: apache-2.0
language: en
base_model: dmis-lab/biobert-base-cased-v1.1
tags:
- biomedical
- named-entity-recognition
- nested-ner
- huntingtons-disease
---

# t2know-biobert: BioBERT span classifier (flat, multi-label BIO)

Baseline model trained on the T2KNOW corpus (Huntington's disease abstracts, 40 UMLS-derived labels, nested and same-span multi-label entities). Three training seeds are provided in `seed_12345/`, `seed_23456/` and `seed_34567/`.

- Base model: [`dmis-lab/biobert-base-cased-v1.1`](https://huggingface.co/dmis-lab/biobert-base-cased-v1.1)
- Corpus: T2KNOW v1.0.1 (Zenodo DOI: [10.5281/zenodo.23801223](https://doi.org/10.5281/zenodo.23801223))
- Paper: under review; the citation will be added on publication.
- Code: release archive `code/` (Zenodo DOI above)

## Usage

The checkpoint uses the custom `BertForSpanClassification` head from the release code (`code/flat_baselines/modeling.py`); it cannot be loaded with a stock `AutoModel` head. Decode with `logit_threshold=0.5` and `start_confidence=0.9` (`decoding.json`), which were selected on the validation split.

## Training data

Trained on the T2KNOW v1.0 document-disjoint train split (validation split for model selection). The v1.0 sentence-level split files contained duplicated sentence records; the corrected release v1.0.1 removes them without changing any annotation. No source text is included in this repository.

## Test results

Overlap-aware scores from the official T2KNOW evaluator (partial span matches count half) on the de-duplicated T2KNOW v1.0.1 test split (151 documents, 1,413 sentences, 12,579 entities). Predictions are the archived outputs of these checkpoints.

| Seed | Precision | Recall | F1 |
|---|---|---|---|
| 12345 | 0.5233 | 0.7396 | 0.6130 |
| 23456 | 0.5309 | 0.7383 | 0.6177 |
| 34567 | 0.5323 | 0.7289 | 0.6153 |

Mean F1 over the three seeds: 0.6153 (population std 0.0019).

## Limitations

Scores measure agreement with the T2KNOW annotation layer (expert-checked, single annotator, see the paper), not semantic correctness. Very-low-support labels are unreliable.

## Licence

Apache License 2.0, following the base model: BioBERT is released under Apache 2.0 (https://github.com/dmis-lab/biobert, LICENSE; the Hugging Face model page carries no licence tag). T2KNOW annotations used for training are MIT-licensed; no third-party source text is included.

Repository: `https://huggingface.co/edugredu/t2know-biobert`