Update transformer version in bert_ranker

deeppavlov/configs/ranking/ranking_ubuntu_v2_torch_bert_uncased.json

@@ -31,6 +31,7 @@
         "class_name": "torch_bert_ranker",
         "one_hot_labels": false,
         "pretrained_bert": "bert-base-uncased",
+        "bert_config_file": "{MODEL_PATH}/model/config.json",
         "save_path": "{MODEL_PATH}/model",
         "load_path": "{MODEL_PATH}/model",
         "optimizer": "AdamW",
@@ -86,7 +87,7 @@
     "download": [
       {
         "url": "http://files.deeppavlov.ai/datasets/ubuntu_v2_data.tar.gz",
-        "subdir": "{DOWNLOADS_PATH}/ubuntu_v2_data"
+        "subdir": "{DOWNLOADS_PATH}/ubuntu_v2_data_"
       },
       {
         "url": "http://files.deeppavlov.ai/deeppavlov_data/ubuntu_v2_uncased_torch_bert_model_v0.tar.gz",

deeppavlov/core/common/requirements_registry.json

@@ -203,7 +203,7 @@
   ],
   "torch_bert_ranker": [
     "{DEEPPAVLOV_PATH}/requirements/pytorch16.txt",
-    "{DEEPPAVLOV_PATH}/requirements/transformers28.txt"
+    "{DEEPPAVLOV_PATH}/requirements/transformers.txt"
   ],
   "torch_transformers_classifier": [
     "{DEEPPAVLOV_PATH}/requirements/pytorch16.txt",
@@ -226,7 +226,7 @@
   ],
   "torch_bert_ranker_preprocessor": [
     "{DEEPPAVLOV_PATH}/requirements/pytorch16.txt",
-    "{DEEPPAVLOV_PATH}/requirements/transformers28.txt"
+    "{DEEPPAVLOV_PATH}/requirements/transformers.txt"
   ],
   "transformers_bert_preprocessor": [
     "{DEEPPAVLOV_PATH}/requirements/transformers.txt"

deeppavlov/models/torch_bert/torch_bert_ranker.py

@@ -47,7 +47,7 @@ class TorchBertRankerModel(TorchModel):
                               e.g. {'lr': 0.1, 'weight_decay': 0.001, 'momentum': 0.9}
     """
 
-    def __init__(self, pretrained_bert: str,
+    def __init__(self, pretrained_bert: str = None,
                  bert_config_file: Optional[str] = None,
                  n_classes: int = 2,
                  return_probas: bool = True,
@@ -97,7 +97,7 @@ def train_on_batch(self, features_li: List[List[InputFeatures]], y: Union[List[i
         self.optimizer.zero_grad()
 
         loss, logits = self.model(b_input_ids, token_type_ids=None, attention_mask=b_input_masks,
-                                  labels=b_labels)
+                                  labels=b_labels, return_dict=False)
         loss.backward()
         # Clip the norm of the gradients to 1.0.
         # This is to help prevent the "exploding gradients" problem.
@@ -162,13 +162,20 @@ def load(self, fname=None):
 
         if self.pretrained_bert:
             log.info(f"From pretrained {self.pretrained_bert}.")
-            config = AutoConfig.from_pretrained(self.pretrained_bert,
-                                                # num_labels=self.n_classes,
-                                                output_attentions=False,
-                                                output_hidden_states=False)
+            if Path(expand_path(self.pretrained_bert)).exists():
+                self.pretrained_bert = str(expand_path(self.pretrained_bert))
+                config = AutoConfig.from_pretrained(self.pretrained_bert,
+                                                    # num_labels=self.n_classes,
+                                                    output_attentions=False,
+                                                    output_hidden_states=False)
+
+            else:
+                config = AutoConfig.from_pretrained(self.pretrained_bert,
+                                                    # num_labels=self.n_classes,
+                                                    output_attentions=False,
+                                                    output_hidden_states=False)
 
             self.model = AutoModelForSequenceClassification.from_pretrained(self.pretrained_bert, config=config)
-
             try:
                 hidden_size = self.model.classifier.out_proj.in_features
 
@@ -188,13 +195,10 @@ def load(self, fname=None):
                     self.model.num_labels = self.n_classes
 
 
-        elif self.bert_config_file and Path(self.bert_config_file).is_file():
-            self.bert_config = AutoConfig.from_json_file(str(expand_path(self.bert_config_file)))
-            if self.attention_probs_keep_prob is not None:
-                self.bert_config.attention_probs_dropout_prob = 1.0 - self.attention_probs_keep_prob
-            if self.hidden_keep_prob is not None:
-                self.bert_config.hidden_dropout_prob = 1.0 - self.hidden_keep_prob
+        elif self.bert_config_file and expand_path(self.bert_config_file).is_file():
+            self.bert_config = AutoConfig.from_pretrained(str(expand_path(self.bert_config_file)))
             self.model = AutoModelForSequenceClassification.from_config(config=self.bert_config)
+
         else:
             raise ConfigError("No pre-trained BERT model is given.")