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---
license: gpl-3.0
datasets:
- medalpaca/medical_meadow_medical_flashcards
pipeline_tag: question-answering
---
# 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/).
<br>
# 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
class newStoppingCriteria(StoppingCriteria):
def __init__(self, stop_word):
self.stop_word = stop_word
def __call__(self, input_ids, scores, **kwargs):
decoded_text = tokenizer.decode(input_ids[0], skip_special_tokens=True)
return self.stop_word in decoded_text
criteria = newStoppingCriteria(stop_word = ".")
stoppingCriteriaList = StoppingCriteriaList([criteria])
```
<br>
- Since the preprocessed text was formatted as "BoS token - Question - EoS token - BoS token - Answer - EoS token," the model generated answers that included the question as well. To resolve this, I implemented a method to remove the question from the generated text, leaving only the answer:
- ```python
outputText = tokenizer.decode(output_ids[0], skip_special_tokens = True)
inputText = tokenizer.decode(inputEncoding.input_ids[0], skip_special_tokens = True)
answer = outputText[len(inputText):].strip()
```
<br>
# Use Example
```python
question = 'What causes Wernicke encephalopathy?'
inputEncoding = tokenizer(question, return_tensors = 'pt').to('cuda')
output_ids = model.generate(
inputEncoding.input_ids,
max_length = 128,
do_sample = True,
temperature = 0.7,
top_p = 0.97,
top_k = 2,
pad_token_id = tokenizer.eos_token_id,
repetition_penalty = 1.2,
stopping_criteria = stoppingCriteriaList
)
outputText = tokenizer.decode(output_ids[0], skip_special_tokens = True)
inputText = tokenizer.decode(inputEncoding.input_ids[0], skip_special_tokens = True)
answer = outputText[len(inputText):].strip()
# 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.
```
<br>
# Training Information
The model was fine-tuned for 3 epochs using the parameters specified in its original repository:
```python
trainingArgs = TrainingArguments(
output_dir = "MedicalFlashcardsMinerva",
evaluation_strategy = "steps",
save_strategy = "steps",
learning_rate = 2e-4,
per_device_train_batch_size = 6,
per_device_eval_batch_size = 6,
gradient_accumulation_steps = 8,
num_train_epochs = 3,
lr_scheduler_type = "cosine",
warmup_ratio = 0.1,
adam_beta1 = 0.9,
adam_beta2 = 0.95,
adam_epsilon = 1e-8,
weight_decay = 0.01,
logging_steps = 100,
report_to = "none",
)
``` |