clean up glue example

examples/run_bert_classifier.py

@@ -309,14 +309,7 @@ def main():
 
                 # define a new function to compute loss values for both output_modes
                 ouputs = model(input_ids, token_type_ids=segment_ids, attention_mask=input_mask, labels=label_ids)
-                loss = 
-
-                if output_mode == "classification":
-                    loss_fct = CrossEntropyLoss()
-                    loss = loss_fct(logits.view(-1, num_labels), label_ids.view(-1))
-                elif output_mode == "regression":
-                    loss_fct = MSELoss()
-                    loss = loss_fct(logits.view(-1), label_ids.view(-1))
+                loss = ouputs[0]
 
                 if n_gpu > 1:
                     loss = loss.mean() # mean() to average on multi-gpu.
@@ -423,15 +416,8 @@ def main():
             label_ids = label_ids.to(device)
 
             with torch.no_grad():
-                logits = model(input_ids, token_type_ids=segment_ids, attention_mask=input_mask)
-
-            # create eval loss and other metric required by the task
-            if output_mode == "classification":
-                loss_fct = CrossEntropyLoss()
-                tmp_eval_loss = loss_fct(logits.view(-1, num_labels), label_ids.view(-1))
-            elif output_mode == "regression":
-                loss_fct = MSELoss()
-                tmp_eval_loss = loss_fct(logits.view(-1), label_ids.view(-1))
+                outputs = model(input_ids, token_type_ids=segment_ids, attention_mask=input_mask, labels=label_ids)
+                tmp_eval_loss, logits = outputs[:2]
 
             eval_loss += tmp_eval_loss.mean().item()
             nb_eval_steps += 1

examples/utils_glue.py

@@ -583,6 +583,7 @@ processors = {
 output_modes = {
     "cola": "classification",
     "mnli": "classification",
+    "mnli-mm": "classification",
     "mrpc": "classification",
     "sst-2": "classification",
     "sts-b": "regression",

pytorch_transformers/tokenization_utils.py

@@ -110,6 +110,24 @@ class PreTrainedTokenizer(object):
 
         return tokenizer
 
+    def tokenize(self, text):
+        raise NotImplementedError
+
+    def convert_tokens_to_ids(self, tokens):
+        raise NotImplementedError
+
+    def convert_ids_to_tokens(self, ids):
+        raise NotImplementedError
+
+    def encode(self, text):
+        raise NotImplementedError
+
+    def decode(self, token_ids, *input, **kwargs):
+        raise NotImplementedError
+
+    def save_vocabulary(self, vocab_path):
+        raise NotImplementedError
+
 
 def clean_up_tokenization(out_string):
     out_string.replace(' .', '.').replace(' ?', '?').replace(' !', '!').replace(' ,', ','