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from transformers import AutoTokenizer, T5ForConditionalGeneration
import torch
def load_model():
model_name = "Salesforce/codet5-base-multi-sum"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name)
model.eval()
model.to(torch.device("cuda" if torch.cuda.is_available() else "cpu"))
return tokenizer, model
def generate_explanation(code, tokenizer, model):
device = model.device
# Final prompt style: generate docstring
input_text = f"generate docstring: {code.strip()}"
input_ids = tokenizer.encode(input_text, return_tensors="pt", truncation=True).to(device)
output = model.generate(input_ids, max_new_tokens=150, early_stopping=True)
return tokenizer.decode(output[0], skip_special_tokens=True)
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