MedQuAD

Fine-Tuned GPT-2 Model for Medical Question Answering

Model Description

This model is a fine-tuned version of GPT-2 on the MedQuAD dataset. The primary objective of this model is to generate accurate and informative responses to medical queries based on the training data.

Training Data

The model was trained on the MedQuAD dataset, which consists of medical questions and answers. The dataset covers a wide range of medical topics and is intended to provide reliable and evidence-based information.

Training Procedure

• Base Model: GPT-2
• Dataset: MedQuAD
• Training Framework: Hugging Face Transformers
• Training Arguments:
  • output_dir="./results"
  • num_train_epochs=1
  • per_device_train_batch_size=4
  • save_steps=10,000
  • save_total_limit=2
  • logging_dir="./logs"

Intended Use

This model is intended for generating responses to medical questions. It can be used in applications such as telemedicine, healthcare chatbots, and medical information retrieval systems.

Limitations

• The model's responses are based on the training data and may not always reflect the most up-to-date medical knowledge.
• Users should always consult a medical professional for accurate and personalized medical advice.

Evaluation

The model's performance was evaluated based on the coherence, accuracy, and relevance of the generated responses. Additional metrics and evaluations can be added as needed.

Licensing

The model is released under the Apache 2.0 license.

Contact Information

For questions or comments about the model, please contact the developer.