metadata
license: apache-2.0
language: en
library_name: transformers
tags:
- deberta
- sequence-classification
- medicine
- telerehabilitation
- MeDeBERTa
- MeDeBERTaBot
metrics:
- name: accuracy
type: accuracy
value: 0.9986
- name: macro_f1
type: f1
value: 0.9987
- name: balanced_accuracy
type: balanced_accuracy
value: 0.9987
- name: auc_micro
type: auc
value: 0.999997
- name: ap_micro
type: average_precision
value: 0.99993
datasets:
- malakhovks/MeDeBERTa
base_model:
- microsoft/deberta-v3-xsmall
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 × 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
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.