roberta added

data/data_loader.py

 from torch.utils.data import Dataset
 import albumentations as A
 from transformers import ViTFeatureExtractor
-from transformers import BertTokenizer, BigBirdTokenizer, XLNetTokenizer
+from transformers import BertTokenizer, RobertaTokenizer, XLNetTokenizer
 
 
 def get_transforms(config):
 
 
 def get_tokenizer(config):
-    if 'bigbird' in config.text_encoder_model:
-        tokenizer = BigBirdTokenizer.from_pretrained(config.text_tokenizer)
+    if 'roberta' in config.text_encoder_model:
+        tokenizer = RobertaTokenizer.from_pretrained(config.text_tokenizer)
     elif 'xlnet' in config.text_encoder_model:
         tokenizer = XLNetTokenizer.from_pretrained(config.text_tokenizer)
     else:

data/twitter/config.py

 
     image_model_name = '../../../../../media/external_10TB/10TB/ghorbanpoor/huggingface/vit-base-patch16-224'
     image_embedding = 768
-    text_encoder_model = "../../../../../media/external_10TB/10TB/ghorbanpoor/huggingface/bert-base-uncased"
+    text_encoder_model = "../../../../../media/external_10TB/10TB/ghorbanpoor/huggingface/roberta-base"
     # text_encoder_model = "/home/faeze/PycharmProjects/new_fake_news_detectioin/bert/bert-base-uncased"
-    text_tokenizer = "../../../../../media/external_10TB/10TB/ghorbanpoor/huggingface/bert-base-uncased"
+    text_tokenizer = "../../../../../media/external_10TB/10TB/ghorbanpoor/huggingface/roberta-base"
     # text_tokenizer = "/home/faeze/PycharmProjects/new_fake_news_detectioin/bert/bert-base-uncased"
     text_embedding = 768
     max_length = 32

text.py

 from torch import nn
-from transformers import BertModel, BertConfig, BertTokenizer, \
-    BigBirdModel, BigBirdConfig, BigBirdTokenizer, \
-    XLNetModel, XLNetConfig, XLNetTokenizer
+from transformers import BertModel, BertConfig, \
+    RobertaModel, RobertaConfig, \
+    XLNetModel, XLNetConfig
 
 
 class TextEncoder(nn.Module):
 
 
 def get_text_model(config):
-    if 'bigbird' in config.text_encoder_model:
+    if 'roberta' in config.text_encoder_model:
         if config.pretrained:
-            model = BigBirdModel.from_pretrained(config.text_encoder_model, block_size=16, num_random_blocks=2)
+            model = RobertaModel.from_pretrained(config.text_encoder_model, output_attentions=False,
+                                                 output_hidden_states=True, return_dict=True)
         else:
-            model = BigBirdModel(config=BigBirdConfig())
+            model = RobertaModel(config=RobertaConfig())
     elif 'xlnet' in config.text_encoder_model:
         if config.pretrained:
             model = XLNetModel.from_pretrained(config.text_encoder_model, output_attentions=False,
-                                              output_hidden_states=True, return_dict=True)
+                                               output_hidden_states=True, return_dict=True)
         else:
             model = XLNetModel(config=XLNetConfig())
     else:
         else:
             model = BertModel(config=BertConfig())
     return model
-