Updated GLUE script to add DistilBERT. Cleaned up unused args in the utils file.

examples/run_glue.py

@@ -39,7 +39,10 @@ from pytorch_transformers import (WEIGHTS_NAME, BertConfig,
                                   XLMConfig, XLMForSequenceClassification,
                                   XLMTokenizer, XLNetConfig,
                                   XLNetForSequenceClassification,
-                                  XLNetTokenizer)
+                                  XLNetTokenizer,
+                                  DistilBertConfig,
+                                  DistilBertForSequenceClassification,
+                                  DistilBertTokenizer)
 
 from pytorch_transformers import AdamW, WarmupLinearSchedule
 
@@ -55,6 +58,7 @@ MODEL_CLASSES = {
     'xlnet': (XLNetConfig, XLNetForSequenceClassification, XLNetTokenizer),
     'xlm': (XLMConfig, XLMForSequenceClassification, XLMTokenizer),
     'roberta': (RobertaConfig, RobertaForSequenceClassification, RobertaTokenizer),
+    'distilbert': (DistilBertConfig, DistilBertForSequenceClassification, DistilBertTokenizer)
 }
 
 
@@ -128,7 +132,7 @@ def train(args, train_dataset, model, tokenizer):
             batch = tuple(t.to(args.device) for t in batch)
             inputs = {'input_ids':      batch[0],
                       'attention_mask': batch[1],
-                      'token_type_ids': batch[2] if args.model_type in ['bert', 'xlnet'] else None,  # XLM and RoBERTa don't use segment_ids
+                      'token_type_ids': batch[2] if args.model_type in ['bert', 'xlnet'] else None,  # XLM, DistilBERT and RoBERTa don't use segment_ids
                       'labels':         batch[3]}
             outputs = model(**inputs)
             loss = outputs[0]  # model outputs are always tuple in pytorch-transformers (see doc)
@@ -218,7 +222,7 @@ def evaluate(args, model, tokenizer, prefix=""):
             with torch.no_grad():
                 inputs = {'input_ids':      batch[0],
                           'attention_mask': batch[1],
-                          'token_type_ids': batch[2] if args.model_type in ['bert', 'xlnet'] else None,  # XLM and RoBERTa don't use segment_ids
+                          'token_type_ids': batch[2] if args.model_type in ['bert', 'xlnet'] else None,  # XLM, DistilBERT and RoBERTa don't use segment_ids
                           'labels':         batch[3]}
                 outputs = model(**inputs)
                 tmp_eval_loss, logits = outputs[:2]
@@ -273,11 +277,6 @@ def load_and_cache_examples(args, task, tokenizer, evaluate=False):
             label_list[1], label_list[2] = label_list[2], label_list[1] 
         examples = processor.get_dev_examples(args.data_dir) if evaluate else processor.get_train_examples(args.data_dir)
         features = convert_examples_to_features(examples, label_list, args.max_seq_length, tokenizer, output_mode,
-            cls_token_at_end=bool(args.model_type in ['xlnet']),            # xlnet has a cls token at the end
-            cls_token=tokenizer.cls_token,
-            cls_token_segment_id=2 if args.model_type in ['xlnet'] else 0,
-            sep_token=tokenizer.sep_token,
-            sep_token_extra=bool(args.model_type in ['roberta']),           # roberta uses an extra separator b/w pairs of sentences, cf. github.com/pytorch/fairseq/commit/1684e166e3da03f5b600dbb7855cb98ddfcd0805
             pad_on_left=bool(args.model_type in ['xlnet']),                 # pad on the left for xlnet
             pad_token=tokenizer.convert_tokens_to_ids([tokenizer.pad_token])[0],
             pad_token_segment_id=4 if args.model_type in ['xlnet'] else 0,

examples/utils_glue.py

@@ -390,22 +390,12 @@ class WnliProcessor(DataProcessor):
 
 def convert_examples_to_features(examples, label_list, max_seq_length,
                                  tokenizer, output_mode,
-                                 cls_token_at_end=False,
-                                 cls_token='[CLS]',
-                                 cls_token_segment_id=1,
-                                 sep_token='[SEP]',
-                                 sep_token_extra=False,
                                  pad_on_left=False,
                                  pad_token=0,
                                  pad_token_segment_id=0,
-                                 sequence_a_segment_id=0, 
-                                 sequence_b_segment_id=1,
                                  mask_padding_with_zero=True):
-    """ Loads a data file into a list of `InputBatch`s
-        `cls_token_at_end` define the location of the CLS token:
-            - False (Default, BERT/XLM pattern): [CLS] + A + [SEP] + B + [SEP]
-            - True (XLNet/GPT pattern): A + [SEP] + B + [SEP] + [CLS]
-        `cls_token_segment_id` define the segment id associated to the CLS token (0 for BERT, 2 for XLNet)
+    """
+    Loads a data file into a list of `InputBatch`s
     """
 
     label_map = {label : i for i, label in enumerate(label_list)}