Model Card: Fine-Tuned Language Model for Medical Reasoning

Model Overview

This language model has been fine-tuned on the FreedomIntelligence/medical-o1-reasoning-SFT. The fine-tuning process focused on enhancing the model's ability to reason and provide contextually accurate predictions within the medical domain. Domain adaptation was achieved through fine-tuning the pretrained model on the medical reasoning dataset using the Masked Language Modeling (MLM) objective. This approach allowed the model to better understand domain-specific terminology and context by predicting masked tokens based on surrounding words. This model can assist in tasks requiring reasoning over medical text, such as medical Q&A, report generation, or other domain-specific NLP applications.

Intended Use

Primary Applications

Medical reasoning tasks.
Text completion or generation in the medical domain.
Context-aware masked token prediction for medical content.

Limitations

This model is fine-tuned for medical reasoning and might not generalize well to other domains.
It is not a substitute for professional medical advice or diagnosis.

Usage Warnings

Outputs should always be reviewed by qualified professionals.
The model may produce incorrect or misleading results when provided with ambiguous or incomplete context.

Fine-Tuning Details

Pretrained Model

Base model: distilbert/distilbert-base-uncased

Fine-Tuning Objective

The model was fine-tuned using the Masked Language Modeling (MLM) objective. Both subword masking and whole word masking strategies were experimented with, but no significant performance difference was observed between the two approaches.

Dataset

Dataset: FreedomIntelligence/medical-o1-reasoning-SFT
Dataset Description: This dataset is designed for reasoning tasks in the medical domain and includes diverse medical scenarios.

Training Setup

Hardware: T4 GPU (Google Colab)
Software: - Transformers 4.48.1 - Pytorch 2.5.1+cu121 - Datasets 3.2.0 - Tokenizers 0.21.0

Performance Metrics

Evaluation Metrics: Cross-entropy loss and perplexity were used to evaluate model performance.
Results:
- Cross-Entropy Loss: 1.2834
- Perplexity: 3.6

Observations

Both subword masking and whole word masking yielded similar performance metrics, indicating no significant advantage of one masking strategy over the other.

Limitations and Biases

The model's performance is dependent on the quality and representativeness of the dataset.
Potential biases in the dataset may propagate into the model outputs.
The model might not handle rare or out-of-distribution medical terms effectively.

Training results

Training Loss	Epoch	Step	Validation Loss
1.4437	1.0	263	1.4178
1.4236	2.0	526	1.3673
1.4316	3.0	789	1.3320
1.4174	4.0	1052	1.3123
1.4015	5.0	1315	1.2988
1.3922	6.0	1578	1.2834

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distilbert-med-v2