remove old methods

run_classifier.py

@@ -305,37 +305,6 @@ def _truncate_seq_pair(tokens_a, tokens_b, max_length):
         else:
             tokens_b.pop()
 
-
-def input_fn_builder(features, seq_length, train_batch_size):
-    # TODO: delete
-    """Creates an `input_fn` closure to be passed to TPUEstimator.""" ### ATTENTION - To rewrite ###
-
-    all_input_ids = [f.input_ids for feature in features]
-    all_input_mask = [f.input_mask for feature in features]
-    all_segment_ids = [f.segment_ids for feature in features]
-    all_label_ids = [f.label_id for feature in features]
-
-    # for feature in features:
-    #     all_input_ids.append(feature.input_ids)
-    #     all_input_mask.append(feature.input_mask)
-    #     all_segment_ids.append(feature.segment_ids)
-    #     all_label_ids.append(feature.label_id)
-
-    input_ids_tensor = torch.tensor(all_input_ids, dtype=torch.Long)
-    input_mask_tensor = torch.tensor(all_input_mask, dtype=torch.Long)
-    segment_tensor = torch.tensor(all_segment_ids, dtype=torch.Long)
-    label_tensor = torch.tensor(all_label_ids, dtype=torch.Long)
-
-    train_data = TensorDataset(input_ids_tensor, input_mask_tensor,
-                               segment_tensor, label_tensor)
-    if args.local_rank == -1:
-        train_sampler = RandomSampler(train_data)
-    else:
-        train_sampler = DistributedSampler(train_data)
-    train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=train_batch_size)
-
-    return train_dataloader
-
 def accuracy(out, labels):
     outputs = np.argmax(out, axis=1)
     return np.sum(outputs==labels)