change bert task reader interface

demo/bert-cls/finetune_with_hub.py

@@ -73,11 +73,11 @@ if __name__ == '__main__':
         warmup_proportion=args.warmup_proportion)
 
     # loading paddlehub BERT
-    # module = hub.Module(
-    #     module_dir="./hub_module/chinese_L-12_H-768_A-12.hub_module")
-    module = hub.Module(module_dir="./hub_module/ernie-stable.hub_module")
+    module = hub.Module(
+        module_dir="./hub_module/chinese_L-12_H-768_A-12.hub_module")
+    # module = hub.Module(module_dir="./hub_module/ernie-stable.hub_module")
 
-    processor = reader.ChnsenticorpProcessor(
+    processor = reader.BERTClassifyReader(
         data_dir=args.data_dir,
         vocab_path=module.get_vocab_path(),
         max_seq_len=args.max_seq_len)

demo/bert-cls/reader/cls.py

@@ -109,9 +109,10 @@ class DataProcessor(object):
 
     def get_num_examples(self, phase):
         """Get number of examples for train, dev or test."""
-        if phase not in ['train', 'dev', 'test']:
+        if phase not in ['train', 'validate', 'test']:
             raise ValueError(
-                "Unknown phase, which should be in ['train', 'dev', 'test'].")
+                "Unknown phase, which should be in ['train', 'validate, 'test']."
+            )
         return self.num_examples[phase]
 
     def get_train_progress(self):
@@ -131,9 +132,9 @@ class DataProcessor(object):
         if phase == 'train':
             examples = self.get_train_examples(self.data_dir)
             self.num_examples['train'] = len(examples)
-        elif phase == 'dev':
+        elif phase == 'validate':
             examples = self.get_dev_examples(self.data_dir)
-            self.num_examples['dev'] = len(examples)
+            self.num_examples['validate'] = len(examples)
         elif phase == 'test':
             examples = self.get_test_examples(self.data_dir)
             self.num_examples['test'] = len(examples)
@@ -190,7 +191,7 @@ class DataProcessor(object):
                     return_input_mask=True,
                     return_max_len=True,
                     return_num_token=False)
-                yield batch_data
+                yield [batch_data]
 
         return wrapper
 
@@ -473,6 +474,41 @@ class ChnsenticorpProcessor(DataProcessor):
         return examples
 
 
+class BERTClassifyReader(DataProcessor):
+    """Processor for the Chnsenticorp data set."""
+
+    def get_train_examples(self, data_dir):
+        """See base class."""
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
+
+    def get_dev_examples(self, data_dir):
+        """See base class."""
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
+
+    def get_test_examples(self, data_dir):
+        """See base class."""
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
+
+    def get_labels(self):
+        """See base class."""
+        return ["0", "1"]
+
+    def _create_examples(self, lines, set_type):
+        """Creates examples for the training and dev sets."""
+        examples = []
+        for (i, line) in enumerate(lines):
+            guid = "%s-%s" % (set_type, i)
+            text_a = tokenization.convert_to_unicode(line[1])
+            label = tokenization.convert_to_unicode(line[0])
+            examples.append(
+                InputExample(
+                    guid=guid, text_a=text_a, text_b=None, label=label))
+        return examples
+
+
 def convert_single_example_to_unicode(guid, single_example):
     text_a = tokenization.convert_to_unicode(single_example[0])
     text_b = tokenization.convert_to_unicode(single_example[1])

demo/bert-cls/run_fintune_with_hub.sh

-export CUDA_VISIBLE_DEVICES=2
+export CUDA_VISIBLE_DEVICES=5
 
 DATA_PATH=./chnsenticorp_data
 
 rm -rf $CKPT_PATH
 python -u finetune_with_hub.py \
-                   --use_cuda true \
                    --batch_size 32 \
                    --in_tokens false \
                    --data_dir ${DATA_PATH} \
                    --weight_decay  0.01 \
                    --warmup_proportion 0.0 \
-                   --validation_steps 50 \
                    --epoch 3 \
                    --max_seq_len 128 \
-                   --learning_rate 5e-5 \
-                   --skip_steps 10
+                   --learning_rate 5e-5

paddle_hub/module/module.py

@@ -255,7 +255,6 @@ class Module:
 
     def get_vocab_path(self):
         for assets_file in self.assets:
-            print(assets_file)
             if "vocab.txt" in assets_file:
                 return assets_file