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bert.pth

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classifier.pth

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config.json

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-{
-  "_name_or_path": "roberta-base",
-  "architectures": [
-    "RobertaForSequenceClassification"
-  ],
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-  "id2label": {
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-  "model_type": "roberta",
-  "num_attention_heads": 12,
-  "num_hidden_layers": 12,
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-  "position_embedding_type": "absolute",
-  "problem_type": "multi_label_classification",
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-}
+{"architectures": ["BERT_for_MID"], "model_type": "roberta", "hidden_size": 768, "num_labels": 147, "model_version": "v1.0"}

modeling_bert_for_mid.py

@@ -0,0 +1,12 @@
+
+import torch
+import torch.nn as nn
+
+class BERT_for_MID(nn.Module):
+    def __init__(self, bert, classifier):
+        super(BERT_for_MID, self).__init__()
+        self.bert = bert
+        self.classifier = classifier
+
+    def forward(self, input, attention_mask):
+        return self.classifier(self.bert(input, attention_mask))