本文整理汇总了Python中pytorch_pretrained_bert.modeling.BertForQuestionAnswering.from_pretrained方法的典型用法代码示例。如果您正苦于以下问题：Python BertForQuestionAnswering.from_pretrained方法的具体用法？Python BertForQuestionAnswering.from_pretrained怎么用？Python BertForQuestionAnswering.from_pretrained使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类pytorch_pretrained_bert.modeling.BertForQuestionAnswering的用法示例。
在下文中一共展示了BertForQuestionAnswering.from_pretrained方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test
# 需要导入模块: from pytorch_pretrained_bert.modeling import BertForQuestionAnswering [as 别名]
# 或者: from pytorch_pretrained_bert.modeling.BertForQuestionAnswering import from_pretrained [as 别名]
def test(args): # Load a trained model that you have fine-tuned (we assume evaluate on cpu)
tokenizer = BertTokenizer.from_pretrained(modelconfig.MODEL_ARCHIVE_MAP[args.bert_model])
eval_examples = data_utils.read_squad_examples(os.path.join(args.data_dir,"test.json"), is_training=False)
eval_features = data_utils.convert_examples_to_features(eval_examples, tokenizer, args.max_seq_length, args.doc_stride, args.max_query_length, is_training=False)
logger.info("***** Running evaluation *****")
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
all_input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long)
all_example_index = torch.arange(all_input_ids.size(0), dtype=torch.long)
eval_data = TensorDataset(all_input_ids, all_segment_ids, all_input_mask, all_example_index)
# Run prediction for full data
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size)
model = torch.load(os.path.join(args.output_dir, "model.pt") )
model.cuda()
model.eval()
all_results = []
for step, batch in enumerate(eval_dataloader):
example_indices = batch[-1]
batch = tuple(t.cuda() for t in batch[:-1])
input_ids, segment_ids, input_mask= batch
with torch.no_grad():
batch_start_logits, batch_end_logits = model(input_ids, segment_ids, input_mask)
for i, example_index in enumerate(example_indices):
start_logits = batch_start_logits[i].detach().cpu().tolist()
end_logits = batch_end_logits[i].detach().cpu().tolist()
eval_feature = eval_features[example_index.item()]
unique_id = int(eval_feature.unique_id)
all_results.append(data_utils.RawResult(unique_id=unique_id,
start_logits=start_logits,
end_logits=end_logits))
output_prediction_file = os.path.join(args.output_dir, "predictions.json")
output_nbest_file = os.path.join(args.output_dir, "nbest_predictions.json")
data_utils.write_predictions(eval_examples, eval_features, all_results, args.n_best_size, args.max_answer_length,
True, output_prediction_file, output_nbest_file, False)