fix paths

examples/run_classifier.py

@@ -380,24 +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(
+        cached_eval_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:
+            with open(cached_eval_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)
+            eval_features = convert_examples_to_features(
+                eval_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)
+                logger.info("  Saving eval features into cached file %s", cached_eval_features_file)
+                with open(cached_eval_features_file, "wb") as writer:
+                    pickle.dump(eval_features, writer)
 
 
-        eval_features = convert_examples_to_features(
-            eval_examples, label_list, args.max_seq_length, tokenizer, output_mode)
         logger.info("***** Running evaluation *****")
         logger.info("  Num examples = %d", len(eval_examples))
         logger.info("  Batch size = %d", args.eval_batch_size)