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1from transformers import T5ForConditionalGeneration, T5Tokenizer
2
3model = T5ForConditionalGeneration.from_pretrained("path/to/model")
4tokenizer = T5Tokenizer.from_pretrained("path/to/model")
5
6def classify_text(question, category="", definition=""):
7 # Prepare your input
8 input_text = f"Question: {question} | Category: {category} | Definition: {definition}"
9 input_ids = tokenizer(input_text, return_tensors="pt", max_length=512, truncation=True).input_ids
10
11 # Get prediction
12 outputs = model.generate(input_ids)
13 predicted_class = tokenizer.decode(outputs[0], skip_special_tokens=True)
14 return predicted_class
15
16# Example usage
17question = "Your question here"
18category = "" # Optional: provide if available
19definition = "" # Optional: provide if available
20predicted_class = classify_text(question, category, definition)
21print(f"Predicted class: {predicted_class}")