Create model.py

model.py

@@ -0,0 +1,25 @@
+from torch import nn
+from transformers import BertModel
+import logging
+from transformers.modeling_outputs import TokenClassifierOutput
+
+
+class BertClassifier(nn.Module):
+    def __init__(self, bert_model="Sifal/dzarabert", num_labels=2, dropout=0.1):
+        super().__init__()
+        self.bert = BertModel.from_pretrained(bert_model)
+        self.num_labels = num_labels
+        self.classifier = nn.Sequential(
+            nn.Linear(self.bert.config.hidden_size, self.bert.config.hidden_size),
+            nn.ReLU(),
+            nn.Dropout(dropout),
+            nn.Linear(self.bert.config.hidden_size, num_labels))
+
+    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
+        if labels is not None:
+            loss_fct = nn.CrossEntropyLoss()
+            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+        return TokenClassifierOutput(loss=loss, logits=logits, hidden_states=output.hidden_states,attentions=output.attentions)