Create README.md

README.md

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+# DistilGPT2 Fine-Tuned on PubMedQA Artificial Subset
+This model is a fine-tuned version of the DistilGPT2 for question-answering tasks in the biomedical domain. The model was trained on a subset of 50,000 artificial samples from the PubMedQA dataset.
+
+## Model Details
+1. Model architecture: DistilGPT2
+2. Training dataset: 50,000 samples from PubMedQA artificial subset
+3. Training epochs: 3
+4. Tokenizer maximum length: 512
+5. Fine-Tuning Details: Model finetuning was done for three epochs using a standard model training pipeline provided by the Huggingface library. During training, the tokenizer was configured with a maximum token length of 512.
+
+# Example Usage
+You can use this model for medical question-answering tasks by simply loading it with the Huggingface transformers library and providing a prompt.
+```
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+
+tokenizer = GPT2Tokenizer.from_pretrained("DistilGPT_PubMedQA")
+model = GPT2LMHeadModel.from_pretrained("DistilGPT_PubMedQA")
+
+prompt = "question: What is the primary function of the liver? context: The liver is a vital organ that performs several essential functions, including detoxification, protein synthesis, and the production of biochemicals necessary for digestion."
+
+input_ids = tokenizer.encode(prompt, return_tensors="pt")
+output = model.generate(input_ids, max_length=100, num_return_sequences=1, no_repeat_ngram_size=2)
+response = tokenizer.decode(output[0], skip_special_tokens=True)
+
+print(response)
+```
+Evaluation Metrics
+Model performance was evaluated using Semantic Textual Similarity, Word Mover's Distance, and Grammar Errors. Detailed evaluation results can be found in the accompanying paper.
+
+Limitations
+While this model has been fine-tuned on a specific biomedical dataset, it may not perform equally well on other medical or general domain questions. Additionally, the model may generate plausible-sounding but incorrect answers. Always verify the generated answers with reliable sources before using them for critical decision-making.
+
+Acknowledgements
+We want to thank Huggingface for their excellent transformers library and the creators of the original DistilGPT2 model. We also thank the authors of the PubMedQA dataset for providing a valuable resource for training and evaluating biomedical question-answering models.