updating examples

examples/run_classifier.py

@@ -343,12 +343,9 @@ def main():
                         type=str,
                         required=True,
                         help="The input data dir. Should contain the .tsv files (or other data files) for the task.")
-    parser.add_argument("--bert_config_file",
-                        default=None,
-                        type=str,
-                        required=True,
-                        help="The config json file corresponding to the pre-trained BERT model. \n"
-                             "This specifies the model architecture.")
+    parser.add_argument("--bert_model", default=None, type=str, required=True,
+                        help="Bert pre-trained model selected in the list: bert-base-uncased, "
+                             "bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese.")
     parser.add_argument("--task_name",
                         default=None,
                         type=str,
@@ -366,14 +363,6 @@ def main():
                         help="The output directory where the model checkpoints will be written.")
 
     ## Other parameters
-    parser.add_argument("--init_checkpoint",
-                        default=None,
-                        type=str,
-                        help="Initial checkpoint (usually from a pre-trained BERT model).")
-    parser.add_argument("--do_lower_case",
-                        default=False,
-                        action='store_true',
-                        help="Whether to lower case the input text. True for uncased models, False for cased models.")
     parser.add_argument("--max_seq_length",
                         default=128,
                         type=int,
@@ -477,13 +466,6 @@ def main():
     if not args.do_train and not args.do_eval:
         raise ValueError("At least one of `do_train` or `do_eval` must be True.")
 
-    bert_config = BertConfig.from_json_file(args.bert_config_file)
-
-    if args.max_seq_length > bert_config.max_position_embeddings:
-        raise ValueError(
-            "Cannot use sequence length {} because the BERT model was only trained up to sequence length {}".format(
-            args.max_seq_length, bert_config.max_position_embeddings))
-
     if os.path.exists(args.output_dir) and os.listdir(args.output_dir):
         raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir))
     os.makedirs(args.output_dir, exist_ok=True)
@@ -496,8 +478,7 @@ def main():
     processor = processors[task_name]()
     label_list = processor.get_labels()
 
-    tokenizer = BertTokenizer(
-        vocab_file=args.vocab_file, do_lower_case=args.do_lower_case)
+    tokenizer = BertTokenizer.from_pretrained(args.bert_model)
 
     train_examples = None
     num_train_steps = None
@@ -507,9 +488,7 @@ def main():
             len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps * args.num_train_epochs)
 
     # Prepare model
-    model = BertForSequenceClassification(bert_config, len(label_list))
-    if args.init_checkpoint is not None:
-        model.bert.load_state_dict(torch.load(args.init_checkpoint, map_location='cpu'))
+    model = BertForSequenceClassification.from_pretrained(args.bert_model, len(label_list))
     if args.fp16:
         model.half()
     model.to(device)