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import json |
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import random |
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def get_qa_type(question): |
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question_type = "other" |
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if "how did the camera" in question.lower() or "is the camera moving" in question.lower(): |
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question_type = "action_sequence" |
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if ("need to go" in question.lower()): |
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question_type = "goal_aim" |
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if "any of the objects in the initial" in question.lower(): |
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question_type = "obj_movement" |
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if "if i" in question.lower(): |
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question_type = "action_consequence" |
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if 'if i move to the' in question.lower() or "for someone at the" in question.lower(): |
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question_type = "perspective" |
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return question_type |
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def convert_to_conversation(json_data): |
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for item in json_data: |
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question = item["question"] |
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answers = item["answers"] |
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correct_answer = item["correct_answer"] |
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item["question_type"] = get_qa_type(question) |
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prompt = question + " Answer the question using a single word or phrase." |
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if len(answers) > 1: |
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ans_choice_order = answers.copy() |
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ans_choice_order = ['"' + ans + '"' for ans in ans_choice_order] |
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random.shuffle(ans_choice_order) |
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answer_choices_format = " or ".join(ans_choice_order) |
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if answer_choices_format != "": |
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prompt += f" Choose between the following options: {answer_choices_format}." |
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item["conversations"] = [ |
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{ |
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"from": "human", |
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"value": prompt |
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}, |
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{ |
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"from": "gpt", |
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"value": correct_answer |
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} |
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] |
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return json_data |
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def process_json_file(input_file, output_file): |
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with open(input_file, 'r', encoding='utf-8') as f: |
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data = json.load(f) |
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enhanced_data = convert_to_conversation(data) |
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with open(output_file, 'w', encoding='utf-8') as f: |
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json.dump(enhanced_data, f, ensure_ascii=False, indent=2) |
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print(f"已处理 {len(enhanced_data)} 条数据,结果保存到 {output_file}") |
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if __name__ == "__main__": |
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process_json_file('train_data.json', 'train_data_convs.json') |
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