Upload BERT_Arch

config.json

@@ -2,6 +2,10 @@
   "architectures": [
     "BERT_Arch"
   ],
+  "auto_map": {
+    "AutoConfig": "pragformer_config.PragFormerConfig",
+    "AutoModel": "model.BERT_Arch"
+  },
   "bert": {
     "_commit_hash": "43cf2d48e8c75d255dccab2a19e40d4774fd8853",
     "_name_or_path": "NTUYG/DeepSCC-RoBERTa",

model.py

@@ -1,6 +1,9 @@
 from transformers import BertPreTrainedModel, AutoModel, PretrainedConfig
+import sys
+sys.path.append("..")
+
 import torch.nn as nn
-from pragformer_config import PragFormerConfig
+from Classifier.pragformer_config import PragFormerConfig
 
 
 

pragformer_config.py

@@ -0,0 +1,28 @@
+from transformers import PretrainedConfig, AutoModel
+
+
+# class PragFormerConfig(PretrainedConfig):
+#     model_type = "pragformer"
+
+#     def __init__(
+#         self,
+#         dropout=0.1,
+#         layers=[768, 512],
+#         **kwargs,
+#     ):
+#         self.dropout = dropout
+#         self.layers = layers
+#         super().__init__(**kwargs)
+
+
+
+class PragFormerConfig(PretrainedConfig):
+    model_type = "pragformer"
+
+    def __init__(self, bert=None, dropout=0.2, fc1=512, fc2=2, softmax_dim=1, **kwargs):
+        self.bert = bert
+        self.dropout = dropout
+        self.fc1 = fc1
+        self.fc2 = fc2
+        self.softmax_dim = softmax_dim
+        super().__init__(**kwargs)