def METHOD_NAME():
    answer_extractor = AnswerExtractor(
        model="uie-base-answer-extractor",
        device=args.device,
        schema=["答案"],
        max_answer_candidates=3,
        position_prob=0.01,
    )
    question_generator = QuestionGenerator(
        model="unimo-text-1.0-question-generation",
        device=args.device,
        num_return_sequences=2,
    )
    qa_filter = QAFilter(
        model="uie-base-qa-filter",
        device=args.device,
        schema=["答案"],
        position_prob=0.1,
    )
    pipe = QAGenerationPipeline(
        answer_extractor=answer_extractor, question_generator=question_generator, qa_filter=qa_filter
    )
    pipeline_params = {"QAFilter": {"is_filter": True}}
    # list example
    meta = [
        "世界上最早的电影院是美国洛杉矶的“电气剧场”，建于1902年。",
        "以脸书为例，2020年时，54%的成年人表示，他们从该平台获取新闻。而现在，这个数字下降到了44%。与此同时，YouTube在过去几年里一直保持平稳，约有三分之一的用户在该平台上获取新闻。",
    ]
    prediction = pipe.run(meta=meta, params=pipeline_params)
    prediction = prediction["filtered_cqa_triples"]
    pprint(prediction)
    # file example
    if args.source_file:
        meta = []
        with open(args.source_file, "r", encoding="utf-8") as rf:
            for line in rf:
                meta.append(line.strip())
        prediction = pipe.run(meta=meta, params=pipeline_params)
        prediction = prediction["filtered_cqa_triples"]
        if not os.path.exists(args.doc_dir):
            os.makedirs(args.doc_dir)
        with open(os.path.join(args.doc_dir, "generated_qa_pairs.txt"), "w", encoding="utf-8") as wf:
            for pair in prediction:
                wf.write(pair["synthetic_question"].strip() + "\t" + pair["synthetic_answer"].strip() + "\n")