Update README.md

README.md

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 # Model Description
 This is a fine-tuned version of the Minerva model, trained on the [Medical Meadow Flashcard Dataset](https://huggingface.co/datasets/medalpaca/medical_meadow_medical_flashcards) for question answering. The model was developed by the Sapienza NLP Team in collaboration with Future Artificial Intelligence Research (FAIR) and CINECA; specifically, I used the version with 350 million parameters due to computational limits, though versions with 1 billion and 3 billion parameters also exist. For more details, please refer to their repositories: [Sapienza NLP on Hugging Face](https://huggingface.co/sapienzanlp) and [Minerva LLMs](https://nlp.uniroma1.it/minerva/).
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 # Issues and possible Solutions
 - In the original fine-tuned version, my model tended to generate answers that continued unnecessarily, leading to repeated sentences and a degradation in quality over time. Parameters like '*max_length*' or '*max_new_tokens*' were ineffective as they merely stopped the generation at a specified point without properly concluding the sentence. To address this issue, I redefined the stopping criteria to terminate the generation at the first period ('.'), as demonstrated in the code below:
 - ```python
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   inputText = tokenizer.decode(inputEncoding.input_ids[0], skip_special_tokens = True)
   answer = outputText[len(inputText):].strip()
   ```
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 # Use Example
 
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   # Generated Answer: Wernicke encephalopathy is caused by a defect in the Wern-Herxheimer reaction, which leads to an accumulation of acid and alkaline phosphatase activity.
   # Effective Answer: The underlying pathophysiologic cause of Wernicke encephalopathy is thiamine (B1) deficiency.
   ```
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 # Training Information
 The model was fine-tuned for 3 epochs using the parameters specified in its original repository: