rm weights download

model.py

@@ -1,13 +1,13 @@
 from torch import nn
-from transformers import BertModel
-import logging
+from transformers import BertModel,BertConfig
 from transformers.modeling_outputs import TokenClassifierOutput
 
 
 class BertClassifier(nn.Module):
-    def __init__(self, bert_model="Sifal/dzarabert", num_labels=2, dropout=0.1):
+    def __init__(self, num_labels=2, dropout=0.1):
         super().__init__()
-        self.bert = BertModel.from_pretrained(bert_model)
+        config = BertConfig(vocab_size=34688, max_position_embeddings=512)
+        self.bert = BertModel(config=config)
         self.num_labels = num_labels
         self.classifier = nn.Sequential(
             nn.Linear(self.bert.config.hidden_size, self.bert.config.hidden_size),
@@ -15,7 +15,7 @@ class BertClassifier(nn.Module):
             nn.Dropout(dropout),
             nn.Linear(self.bert.config.hidden_size, num_labels))
 
-    def forward(self, input_ids=None, attention_mask=None,labels=None, *args, **kwargs):
+    def forward(self, input_ids=None, attention_mask=None,labels=None):
         output = self.bert(input_ids, attention_mask=attention_mask)
         logits = self.classifier(output.pooler_output)
         loss = None