Update README.md

README.md

@@ -9,9 +9,8 @@ tags: [transformers, T5, question-answering]
 
 This model is a fine-tuned version of the T5-small model specifically tailored for question answering tasks in the biomedical domain. It has been trained to understand and generate responses based on biomedical literature, making it particularly useful for researchers and practitioners in the field.
 
-## How to Get Started with the Model
+## Getting started with the model
 
-You can use the following Python code:
 
 ```python
 pip install transformers
@@ -19,16 +18,13 @@ pip install transformers
 from transformers import T5ForConditionalGeneration, T5Tokenizer
 import torch
 
-# Load model and tokenizer
 tokenizer = T5Tokenizer.from_pretrained("starman76/t5_500")
 model = T5ForConditionalGeneration.from_pretrained("starman76/t5_500")
 
-# Prepare the question and context
 context = "Aspirin is a medication used to reduce pain, fever, or inflammation."
 question = "What is Aspirin used for?"
 inputs = tokenizer(question, context, add_special_tokens=True, return_tensors="pt", max_length=512, truncation=True)
 
-# Generate an answer
 with torch.no_grad():
     outputs = model.generate(input_ids=inputs['input_ids'], attention_mask=inputs['attention_mask'], max_length=50)
 answer = tokenizer.decode(outputs[0], skip_special_tokens=True)