updating classification example

examples/run_classifier.py

@@ -228,10 +228,10 @@ def main():
 
         # Prepare data loader
         train_examples = processor.get_train_examples(args.data_dir)
-        cached_train_features_file = args.data_dir + '_{0}_{1}_{2}'.format(
+        cached_train_features_file = os.path.join(args.data_dir, 'train_{0}_{1}_{2}'.format(
             list(filter(None, args.bert_model.split('/'))).pop(),
                         str(args.max_seq_length),
-                        str(task_name))
+                        str(task_name)))
         try:
             with open(cached_train_features_file, "rb") as reader:
                 train_features = pickle.load(reader)
@@ -311,7 +311,7 @@ def main():
                 input_ids, input_mask, segment_ids, label_ids = batch
 
                 # define a new function to compute loss values for both output_modes
-                logits = model(input_ids, segment_ids, input_mask, labels=None)
+                logits = model(input_ids, segment_ids, input_mask)
 
                 if output_mode == "classification":
                     loss_fct = CrossEntropyLoss()
@@ -380,6 +380,22 @@ def main():
     ### Evaluation
     if args.do_eval:
         eval_examples = processor.get_dev_examples(args.data_dir)
+        cached_train_features_file = os.path.join(args.data_dir, 'dev_{0}_{1}_{2}'.format(
+            list(filter(None, args.bert_model.split('/'))).pop(),
+                        str(args.max_seq_length),
+                        str(task_name)))
+        try:
+            with open(cached_train_features_file, "rb") as reader:
+                train_features = pickle.load(reader)
+        except:
+            train_features = convert_examples_to_features(
+                train_examples, label_list, args.max_seq_length, tokenizer, output_mode)
+            if args.local_rank == -1 or torch.distributed.get_rank() == 0:
+                logger.info("  Saving train features into cached file %s", cached_train_features_file)
+                with open(cached_train_features_file, "wb") as writer:
+                    pickle.dump(train_features, writer)
+
+
         eval_features = convert_examples_to_features(
             eval_examples, label_list, args.max_seq_length, tokenizer, output_mode)
         logger.info("***** Running evaluation *****")
@@ -414,7 +430,7 @@ def main():
             label_ids = label_ids.to(device)
 
             with torch.no_grad():
-                logits = model(input_ids, segment_ids, input_mask, labels=None)
+                logits = model(input_ids, segment_ids, input_mask)
 
             # create eval loss and other metric required by the task
             if output_mode == "classification":