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---
library_name: transformers
license: apache-2.0
datasets:
- raidium/ECNQA_generated_questions
- raidium/ECN-QA
language:
- en
metrics:
- accuracy
tags:
- medical
base_model: stanford-crfm/BioMedLM
---
# Model Card for Raidium MQG model
The model is introduced in the paper "Efficient Medical Question Answering with Knowledge-Augmented Question Generation".
Paper: [https://arxiv.org/abs/2405.14654](https://arxiv.org/abs/2405.14654)
MQG is is a transformer language model pre-trained on a series of medical textbooks, and medical questions generated by GPT-4. The weights are initialized with
[BioMedLM](https://huggingface.co/stanford-crfm/BioMedLM), then further pre-trained on those datasets.
The questions have been generated from prompt containing medical data from the textbooks.
They are available here: [ECNQA_generated_questions](https://huggingface.co/datasets/raidium/ECNQA_generated_questions).
MQG is designed to be fine-tuned for Medical Question Answering tasks.
## Model Details
### Model Description
In the expanding field of language model applications, medical knowledge representation remains a significant challenge due to the specialized nature of the domain.
Large language models, such as GPT-4, obtain reasonable scores on medical question answering tasks, but smaller models are far behind.
In this work, we introduce a method to improve the proficiency of a small language model in the medical domain by employing a two-fold approach.
We first fine-tune the model on a corpus of medical textbooks. Then, we use GPT-4 to generate questions similar to the downstream task, prompted with textbook knowledge, and use them to fine-tune the model.
We show the benefits of our training strategy on a medical answering question dataset.
### Using the model
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("raidium/MQG")
model = AutoModelForCausalLM.from_pretrained("raidium/MQG")
```
- **Developed by:** Raidium
- **Model type:** Transformer
- **License:** Aopache 2.0
- **Finetuned from model:** [BioMedLM](https://huggingface.co/stanford-crfm/BioMedLM)
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [https://github.com/raidium-med/MQG]
- **Paper:** [https://arxiv.org/abs/2405.14654](https://arxiv.org/abs/2405.14654)
## Uses
### Direct Use
MQG is trained using next-token-prediction on generated questions.
Therefore, it can be used out-of-the-box to generate potential answers for medical question answering tasks.
However, the generated questions might contain some errors, so it is advised to fine-tune the model on your dataset, and use the models to rank the potential answers.
### Downstream Use
MQG can be fine-tuned for Medical Question Answering tasks.
For multiple choice questions, a classification head should be appended at the end of the model, to rank different proposed answers.
### Out-of-Scope Use
This model should not be used for datasets outside medical tasks.
## Bias, Risks, and Limitations
There is no guarantee that the model answers medical questions correctly. It should only be used for academic purposes, and not in clinical care.
## Training Details
### Training Data
The model is trained on a corpus of medical textbooks, and further pre-trained on generated questions: [ECNQA_generated_questions](https://huggingface.co/datasets/raidium/ECNQA_generated_questions).
### Training Procedure
MGQ is trained using next-token-prediction on both datasets.
#### Training Hyperparameters
- **Training regime:** fp16 mixed-precision training. <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
We tested the model on a medical question answering dataset, ECN-QA, based on the french medical residency examination.
It is composed of "single" and "progressive" questions (i.e a serie of multiple related questions).
It is a multiple-choice question dataset, containing 5 propositions for each question.
#### Metrics
We use the accuracy to evaluate the model on Medical Question Answering.
### Results
See paper: [https://arxiv.org/abs/2405.14654](https://arxiv.org/abs/2405.14654)
### Model Architecture and Objective
The model is based on BioMedLM's architecture, which is modified from GPT-2 architecture.
### Compute Infrastructure
#### Hardware
The model was trained on the Jean-Zay supercomputer, on multiple nodes with 4 A100 gpus.
#### Software
Pytorch, DeepSpeed
## Citation
**BibTeX:**
```
@article{khlaut2024efficient,
title={Efficient Medical Question Answering with Knowledge-Augmented Question Generation},
author={Khlaut, Julien and Dancette, Corentin and Ferreres, Elodie and Bennani, Alaedine and H{\'e}rent, Paul and Manceron, Pierre},
journal={Clinical NLP Workshop, NAACL 2024},
year={2024}
}
```
## Model Card Contact
julien.khlaut at raidium.fr |