	Value
Epochs	20 (best @ epoch 17)
Batch / Grad. Accum.	16 / 4 (eff. 64)
Learning rate	5 × 10⁻⁵
Best val. accuracy	0.99855
Test accuracy	0.99859
Macro F1 (test)	0.99867
Balanced accuracy (test)	0.99868
Micro AUC	0.999997
Micro average precision	0.99993
Loss (val / test)	0.01371 / 0.01305
Hardware	RTX 2080 Ti (11 GB)

Per-class metrics (excerpt)

Label	Precision	Recall	F1	Support
any_code	1.000	1.000	1.000	980
contexts	0.988	0.987	0.988	923
treatment summary	1.000	0.998	0.999	927
…	…	…	…	…

Full table: see classification_report.json / classification_report.csv.

Training

The full fine-tuning pipeline (data prep → training → evaluation scripts) is maintained in the companion GitHub repo
▶ MeDeBERTaBot · deberta_fine_tuning

Usage

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification

tok = AutoTokenizer.from_pretrained("malakhovks/MeDeBERTa")
model = AutoModelForSequenceClassification.from_pretrained("malakhovks/MeDeBERTa")

inputs = tok("what are contraindications for TENS?", return_tensors="pt")
pred   = model(**inputs).logits.argmax(-1).item()
print(model.config.id2label[pred])

Changelog

See CHANGELOG.md for full version history.