added custom config

config.json

@@ -4,6 +4,7 @@
     "BertForSequenceClassification"
   ],
   "auto_map": {
+    "AutoConfig": "config_tunbert.TunBertConfig",
     "AutoModelForSequenceClassification": "modeling_tunbert.TunBERT"
   },
   "attention_probs_dropout_prob": 0.1,

config_TunBERT.py

@@ -0,0 +1,44 @@
+from transformers import PretrainedConfig
+class TunBertConfig(PretrainedConfig):
+    model_type = "bert"
+    def __init__(self, 
+                attention_probs_dropout_prob = 0.1,
+                classifier_dropout = None,
+                gradient_checkpointing = False,
+                hidden_act = "gelu",
+                hidden_dropout_prob = 0.1,
+                hidden_size = 768,
+                initializer_range = 0.02,
+                intermediate_size = 3072,
+                layer_norm_eps =  1e-12,
+                max_position_embeddings =  512,
+                model_type =  "bert",
+                num_attention_heads = 12,
+                num_hidden_layers = 12,
+                pad_token_id =  0,
+                position_embedding_type =  "absolute",
+                transformers_version =  "4.35.2",
+                type_vocab_size = 2,
+                use_cache = True,
+                vocab_size = 30522, 
+                **kwargs):
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.classifier_dropout = classifier_dropout
+        self.gradient_checkpointing = gradient_checkpointing
+        self.hidden_act = hidden_act
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.hidden_size = hidden_size
+        self.initializer_range = initializer_range
+        self.intermediate_size = intermediate_size
+        self.layer_norm_eps = layer_norm_eps
+        self.max_position_embeddings = max_position_embeddings
+        self.model_type = model_type
+        self.num_attention_heads = num_attention_heads
+        self.num_hidden_layers = num_hidden_layers
+        self.pad_token_id = pad_token_id
+        self.position_embedding_type = position_embedding_type
+        self.transformers_version = transformers_version
+        self.type_vocab_size = type_vocab_size
+        self.use_cache = use_cache
+        self.vocab_size = vocab_size
+        super().__init__(**kwargs)

modeling_tunbert.py

@@ -1,7 +1,7 @@
 import torch.nn as nn
-from transformers import AutoTokenizer, AutoModelForMaskedLM, AutoModelForSequenceClassification, PreTrainedModel,AutoConfig, BertModel
+from transformers import  PreTrainedModel, BertModel
 from transformers.modeling_outputs import SequenceClassifierOutput
-from transformers.models.bert.configuration_bert import BertConfig
+from .config_TunBERT import TunBertConfig
 class classifier(nn.Module):
   def __init__(self,config):
     super().__init__()
@@ -14,7 +14,7 @@ class classifier(nn.Module):
 
 
 class TunBERT(PreTrainedModel):
-    config_class = BertConfig
+    config_class = TunBertConfig
     def __init__(self, config):
         super().__init__(config)
         self.BertModel = BertModel(config)
@@ -27,6 +27,10 @@ class TunBERT(PreTrainedModel):
       logits = self.classifier(sequence_output)
       loss =None
       if labels is not None :
-        loss_func = nn.CrossentropyLoss()
+        loss_func = nn.CrossEntropyLoss()
         loss = loss_func(logits.view(-1,self.config.type_vocab_size),labels.view(-1))
-      return SequenceClassifierOutput(loss = loss, logits= logits, hidden_states=outputs.last_hidden_state,attentions=outputs.attentions)
+      return SequenceClassifierOutput(loss = loss, logits= logits, hidden_states=outputs.last_hidden_state,attentions=outputs.attentions)
+    
+
+TunBertConfig.register_for_auto_class()
+TunBERT.register_for_auto_class("AutoModelForSequenceClassification")