predict 结果是no answer

leon2milan commented 2 years ago

下载chinese_pretrain_mrc_roberta_wwm_ext_large 到本地参数为：

model_name = "roberta_wwm_ext_large" # "chinese_pretrain_mrc_macbert_large"

model_type = 'bert'
threads = 24
eval_batch_size = 64
max_seq_length = 384
max_query_length = 32
doc_stride = 128
n_best_size = 10
max_answer_length = 384
do_lower_case = True
version_2_with_negative = False
null_score_diff_threshold = 0.0
verbose_logging = False

output_prediction_file = None
output_nbest_file = None
output_null_log_odds_file = None

predict 代码如下

examples = []
example = SquadExample(
                    qas_id='f388cc7597def765543c1a6d00b19018',
                    question_text='张家港汽车站在哪里',
                    context_text="""1.张家港北站:位于南丰镇辖区内,北至内河泗兴港,南至市铁路专用线,西至沪通铁路,东至规划经四路(双丰公路西侧),面积约
2.6平方公里。 是货运站,沪通铁路沿线办理货运的中间站,设正线2条,到发线3条,有效长1050m。运输品类主要为集装箱、零担、笨重粗杂等。还设货场一处,牵出
线1条,有效长350m。货场初期占地185亩,其中围墙内装卸区100.7亩。货场内设货物线两条,具备笨重粗杂线装卸、仓库站台线装、粗杂货区装卸和仓库等功能。 >站点最新进展:目前,张家港北站范围的路基施工作业已完成,货场暂时用作铺轨基地,站区配套用房计划与张家港站同步实施。图片来源:张家港新闻 2.张家港站:>位于塘桥镇新204国道东侧,人民路北侧。 站前广场为两层结构,负二层是地铁车站,负一层是地下停车场。站前广场工程涉及地上空间广场、道路以及地下停车场>与地铁站,其中,地下停车场可容纳800辆车停放。本项目建设内容分为站前核心区(主要为广场和道路等)和铁路站场桥下区(主要为停车场和地铁区间等),总用地面
积约7.4公顷,总建筑面积约6.7万㎡。 站点最新进展:2020年7月1日,站房和站前广场将与沪通铁路同步投运。 ▽效果图""",
                    answer_text='',
                    start_position_character=None,
                    is_impossible=True,
                    answers=[],
                )
examples.append(example)
features, dataset = squad_convert_examples_to_features_orig(
            examples=examples,
            tokenizer=tokenizer,
            max_seq_length=max_seq_length,
            doc_stride=doc_stride,
            max_query_length=max_query_length,
            is_training=False,
            return_dataset="pt",
            threads=threads,
        )

eval_sampler = SequentialSampler(dataset)
eval_dataloader = DataLoader(dataset, sampler=eval_sampler, batch_size=eval_batch_size)

def to_list(tensor):
    return tensor.detach().cpu().tolist()

all_results = []
for batch in tqdm(eval_dataloader, desc="Evaluating"):
    model.eval()
    batch = tuple(t.to('cuda') for t in batch)

    with torch.no_grad():
        inputs = {
            "input_ids": batch[0],
            "attention_mask": batch[1],
            "token_type_ids": batch[2],
        }
        example_indices = batch[3]
        if model_type in ["xlm", "roberta", "distilbert", "camembert"]:
            del inputs["token_type_ids"]
        outputs = model(**inputs)

    for i, example_index in enumerate(example_indices):
        eval_feature = features[example_index.item()]
        unique_id = int(eval_feature.unique_id)

        output = [to_list(output[i]) for output in outputs]
        start_logits, end_logits = outputs
        result = SquadResult(unique_id, start_logits, end_logits)
        all_results.append(result)

predictions = compute_predictions_logits(
            examples,
            features,
            all_results,
            n_best_size,
            max_answer_length,
            do_lower_case,
            output_prediction_file,
            output_nbest_file,
            output_null_log_odds_file,
            verbose_logging,
            version_2_with_negative,
            null_score_diff_threshold,
            tokenizer
        )

返回结果为no answer