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| from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline | |
| import gradio as gr | |
| from datasets import load_dataset | |
| # Load the UFO dataset from Hugging Face in chunks | |
| dataset = load_dataset('your_dataset_name', split='train', streaming=True) | |
| mdl_name = "deepset/roberta-base-squad2" | |
| my_pipeline = pipeline('question-answering', model=mdl_name, tokenizer=mdl_name) | |
| def answer_question(question): | |
| # Iterate over chunks of the dataset | |
| for chunk in dataset: | |
| # Convert the chunk to a string to use as the context | |
| context = ' '.join([str(item) for item in chunk]) | |
| response = my_pipeline({'question': question, 'context': context}) | |
| if response['score'] > 0.5: # Adjust this threshold as needed | |
| return response | |
| return "No answer found." | |
| gr.Interface(answer_question, inputs="text", outputs="text").launch() | |