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fix merge

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thomwolf 2019-12-21 13:31:28 +01:00
parent 562f864038
commit 8a2be93b4e
1 changed files with 0 additions and 18 deletions
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18
examples/run_squad.py
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@ -374,24 +374,6 @@ def load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=Fal
if args.local_rank == 0 and not evaluate: if args.local_rank == 0 and not evaluate:
torch.distributed.barrier() # Make sure only the first process in distributed training process the dataset, and the others will use the cache torch.distributed.barrier() # Make sure only the first process in distributed training process the dataset, and the others will use the cache
# Convert to Tensors and build dataset
all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long)
all_cls_index = torch.tensor([f.cls_index for f in features], dtype=torch.long)
all_p_mask = torch.tensor([f.p_mask for f in features], dtype=torch.float)
if evaluate:
all_example_index = torch.arange(all_input_ids.size(0), dtype=torch.long)
dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids,
all_example_index, all_cls_index, all_p_mask)
else:
all_start_positions = torch.tensor([f.start_position for f in features], dtype=torch.long)
all_end_positions = torch.tensor([f.end_position for f in features], dtype=torch.long)
all_is_impossible = torch.tensor([1.0 if f.is_impossible == True else 0.0 for f in features], dtype=torch.float)
dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids,
all_start_positions, all_end_positions,
all_cls_index, all_p_mask, all_is_impossible)
if output_examples: if output_examples:
return dataset, examples, features return dataset, examples, features
return dataset return dataset