A fully fine-tuned discriminative model based on microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext on the MedMCQA dataset — 182K medical multiple-choice questions covering 21 subjects from Indian medical entrance exams (AIIMS/PG style).

Fine-tuned as part of an SUTD Master's deep learning course project comparing zero-shot, LoRA, and full SFT approaches on medical MCQ benchmarks.

Model Details

Developed by: James Oon (@jamezoon), SUTD MSTR-DAIE Deep Learning Project
Model type: Encoder-only (BERT-style) + linear classification head (discriminative MCQ)
Base model: microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext (110M parameters, 768 hidden dim)
Language: English
License: MIT
Artefact layout: encoder/ (tokenizer + backbone), mcqa_head.pt (linear classifier state dict), mcqa_metadata.json

Intended Use

Medical multiple-choice question answering. Given a clinical question and 4 options (A–D), the model scores each [context | question + option] pair independently and selects the highest-scoring option. Subjects covered include Physiology, Anatomy, Biochemistry, Pathology, Pharmacology, Surgery, Medicine, Dental, Gynaecology, Paediatrics, and more.

Not intended for real clinical decision-making. This is a research/educational model.

How It Works

Unlike generative models, this is a discriminative approach: each of the 4 options is scored individually by encoding [question + option_i] (optionally prepending the context/explanation from the exp field), and the option with the highest logit wins.

Input:  [CLS] context [SEP] question + option_A [SEP]   → logit_A
        [CLS] context [SEP] question + option_B [SEP]   → logit_B
        [CLS] context [SEP] question + option_C [SEP]   → logit_C
        [CLS] context [SEP] question + option_D [SEP]   → logit_D

Output: softmax([logit_A, logit_B, logit_C, logit_D]) → argmax → predicted option

How to Get Started

python

import os
import torch
from torch import nn
from transformers import AutoTokenizer, AutoModel
from huggingface_hub import snapshot_download

REPO_ID = "jamezoon/medmcqa-pubmedbert-mcqa"
LETTERS = ["A", "B", "C", "D"]

local_dir = snapshot_download(REPO_ID)
encoder_dir = os.path.join(local_dir, "encoder")

tokenizer = AutoTokenizer.from_pretrained(encoder_dir)
encoder = AutoModel.from_pretrained(encoder_dir)
encoder.eval()

head = nn.Linear(768, 1)
head.load_state_dict(torch.load(os.path.join(local_dir, "mcqa_head.pt"), map_location="cpu"))
head.eval()


def predict(question: str, options: list, context: str = None, max_len: int = 192):
    pairs = [
        (context, question + " " + opt) if context else question + " " + opt
        for opt in options
    ]
    enc = tokenizer.batch_encode_plus(
        pairs, truncation=True, padding="max_length", max_length=max_len, return_tensors="pt"
    )
    with torch.no_grad():
        pooled = encoder(**enc).pooler_output   # (4, 768)
        logits = head(pooled).squeeze(-1)       # (4,)
    probs = torch.softmax(logits, dim=0)
    pred = int(torch.argmax(probs).item())
    return LETTERS[pred], {L: round(float(p), 3) for L, p in zip(LETTERS, probs)}


question = "Which of the following is the most common cause of mitral stenosis?"
options = ["Rheumatic fever", "Congenital", "Infective endocarditis", "SLE"]
letter, probs = predict(question, options)
print(f"Predicted: {letter}")
print(probs)

Training Details

Dataset

MedMCQA — 182,822 training samples, 4,183 validation samples (dev set)
21 medical subjects (Dental, Surgery, Medicine, Pathology, Pharmacology, etc.)
Each sample: question + 4 options + correct answer (1-indexed) + explanation
use_context=True: the expert explanation (exp field) is prepended as context during both training and inference

Training Procedure

Hyperparameter	Value
Epochs	2 (with early stopping, patience = 2)
Per-device batch size	16
Learning rate	2e-4
Optimizer	AdamW (eps = 1e-8)
Max sequence length	192 tokens
Hidden dropout	0.4
Early stopping monitor	`val_loss` (min)
Training loss	CrossEntropyLoss over 4-way option logits

Model Architecture

Component	Details
Backbone	PubMedBERT-base (12 layers, 768 hidden, 12 heads)
Trainable parameters	~110M (full fine-tuning)
Classification head	`Linear(768 → 1)`, trained from scratch
Pooling	CLS-token pooler output

Hardware & Training Time

Hardware: NVIDIA GPU (CUDA) or Apple Silicon (MPS, detected automatically)
Framework: PyTorch 2.x, HuggingFace Transformers, PyTorch Lightning

Evaluation

Training Loss Progression

Epoch	Val Loss	Val Acc (Lightning)
0	1.267	33.2%
1	1.250	35.3%
Best checkpoint	1.23	36.0%

Val acc figures above are as logged by the PyTorch Lightning trainer. Due to a known tensor aggregation behaviour in the old EvalResult API (logit tensors averaged across batches instead of concatenated), these slightly overstate the true accuracy. See final inference results below.

Final MCQ Accuracy (Dev Split, 4,183 samples)

Computed from the best-checkpoint predictions saved in dev_results.csv:

Metric	Value
Overall accuracy	29.74% (1,244 / 4,183)
Macro-averaged accuracy	31.60%
Random-chance baseline	25.00%

Per-Subject Accuracy (Dev Split)

Subject	Accuracy	n
Forensic Medicine	43.3%	67
Radiology	42.0%	69
Pediatrics	37.2%	234
Physiology	35.7%	171
Pharmacology	34.2%	243
ENT	34.0%	53
Anaesthesia	32.4%	34
Social & Preventive Medicine	31.8%	129
Gynaecology & Obstetrics	31.7%	224
Psychiatry	31.2%	16
Pathology	29.4%	337
Surgery	29.0%	369
Anatomy	28.2%	234
Ophthalmology	27.6%	58
Dental	27.4%	1,318
Biochemistry	26.9%	171
Medicine	26.1%	295
Orthopaedics	25.0%	20
Microbiology	23.0%	122
Skin	17.6%	17

Best subjects: Forensic Medicine (43.3%), Radiology (42.0%). Weakest: Skin (17.6%), Microbiology (23.0%). Dental dominates the eval set (1,318 / 4,183 = 31.5%), and its 27.4% accuracy pulls the overall figure close to random chance. The model beats random chance (25%) in 16 of 20 subjects.

MCQ Accuracy Comparison (Dev Split, 4,183 samples)

Model	Overall Acc	Macro Acc	Notes
PubMedBERT SFT (this model)	29.7%	31.6%	Discriminative encoder, argmax over 4 logits
Gemma-3-4B-IT zero-shot	TBD	TBD	Generative baseline pending
Gemma-3-4B-IT + LoRA	45.4%	43.3%	Generative, 4B params

Note on comparability: PubMedBERT accuracy is computed as direct argmax accuracy over 4 option logits (discriminative). The generative LoRA models produce free-form text and accuracy is measured by extracting the final A/B/C/D answer. Token accuracy reported during LoRA training (77–79%) is a next-token prediction metric and is not directly comparable to MCQ answer accuracy.

Comparison with Other MedMCQA Models in this Project

	This model (PubMedBERT)	Gemma-3-4B adapter	Qwen3.5-9B adapter	Qwen3-14B adapter
Base model params	110M	4B	9B	14B
Approach	Discriminative (full SFT)	Generative (LoRA)	Generative (LoRA)	Generative (LoRA)
Architecture	Encoder-only (BERT)	Dense transformer	Hybrid (GatedDeltaNet)	Standard transformer
Trainable params	~110M (100%)	12.9M (0.299%)	7.1M (0.079%)	21.0M (0.142%)
Best val loss	1.23 (CE, 4-way)	1.094 (NLL, generative)	0.9669 (NLL)	0.9649 (NLL)
Val MCQ acc (overall)	29.7%	45.4%	TBD	TBD
Val MCQ acc (macro)	31.6%	43.3%	TBD	TBD

Citation

If you use this model, please cite the MedMCQA dataset:

bibtex

@inproceedings{pmlr-v174-pal22a,
  title     = {MedMCQA: A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering},
  author    = {Pal, Ankit and Umapathi, Logesh Kumar and Sankarasubbu, Malaikannan},
  booktitle = {Proceedings of the Conference on Health, Inference, and Learning},
  year      = {2022},
  publisher = {PMLR}
}

Framework Versions

PyTorch 2.x
Transformers (latest as of March 2026)
PyTorch Lightning