Update README.md

README.md

@@ -36,30 +36,23 @@ This model is a fine-tuned version of GPT-2 for medical chatbot in the Indonesia
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
 
-# Load pre-trained model and tokenizer
 model_name = "lafarizo/indo_medical_gpt2_v2"
 model = AutoModelForCausalLM.from_pretrained(model_name)
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 
-# Ensure the model is on the correct device (GPU or CPU)
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 model.to(device)
 
-# Ensure pad_token is set to avoid issues during generation
 if tokenizer.pad_token is None:
     tokenizer.pad_token = tokenizer.eos_token
 
-# Take input from the user
 input_text = input("Pertanyaan: ")
 
-# Tokenize the input text
 inputs = tokenizer(input_text, return_tensors="pt", truncation=True, padding=True, max_length=512)
 
-# Move tensors to the same device as the model
 input_ids = inputs['input_ids'].to(device)
 attention_mask = inputs['attention_mask'].to(device)
 
-# Generate output from the model
 outputs = model.generate(
     input_ids=input_ids,
     attention_mask=attention_mask,
@@ -74,12 +67,9 @@ outputs = model.generate(
     pad_token_id=tokenizer.pad_token_id
 )
 
-# Decode the output and remove input question from the generated answer
 generated_answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
 
-# Remove the input question from the generated answer if it repeats
 if generated_answer.lower().startswith(input_text.lower()):
     generated_answer = generated_answer[len(input_text):].strip()
 
-# Output the answer
 print("Jawaban: ", generated_answer)