MeDeBERTa – v2 (July 2025)

Fine-tuned microsoft/deberta-v3-xsmall on 269 874 Q-A pairs (30 intent labels) for the MeDeBERTaBot medicine question-classification task.

Value
Epochs 20 (best @ epoch 17)
Batch / Grad. Accum. 16 / 4 (eff. 64)
Learning rate 5 Γ— 10⁻⁡
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 β†’ training β†’ evaluation scripts) is maintained in the companion GitHub repo
β–Ά MeDeBERTaBot Β· deberta_fine_tuning

GitHub stars

Usage

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.

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