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# Install required libraries !pip install transformers torch datasets from transformers import pipeline def load_model(): """ Load the DistilBERT model and tokenizer for question answering. Returns a Hugging Face pipeline for question answering. """ print("Loading model...") qa_pipeline = pipeline("question-answering", model="distilbert-base-uncased-distilled-squad") print("Model loaded successfully!") return qa_pipeline def answer_question(qa_pipeline, context, question): """ Use the model to answer a question based on the provided context. Args: - qa_pipeline: Hugging Face pipeline object for question answering - context: The context in which to search for the answer - question: The question to answer Returns: - Answer text generated by the model """ result = qa_pipeline(question=question, context=context) return result['answer'] def main(): # Load the model qa_pipeline = load_model() # Sample context for testing context = """ OpenAI is an AI research and deployment company. Our mission is to ensure that artificial general intelligence benefits all of humanity. OpenAI conducts research in a variety of fields, including natural language processing, machine learning, and artificial intelligence safety. """ print("Welcome to the Question-Answering Model!") print("You can ask questions based on the provided context.") print("\nContext:") print(context) while True: # Get user input question = input("\nEnter your question (or type 'exit' to quit): ") if question.lower() == 'exit': print("Goodbye!") break # Answer the question answer = answer_question(qa_pipeline, context, question) print(f"Answer: {answer}") if __name__ == "__main__": main()
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