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config.json

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+{
+  "_name_or_path": "bert-base-uncased",
+  "architectures": [
+    "CustomBertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "auto_map": {
+    "AutoModelForSequenceClassification": "modeling.CustomBertForSequenceClassification"
+  },
+  "classifier_dropout": null,
+  "custom_head_hidden_size": 256,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.2,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2",
+    "3": "LABEL_3",
+    "4": "LABEL_4",
+    "5": "LABEL_5",
+    "6": "LABEL_6",
+    "7": "LABEL_7",
+    "8": "LABEL_8",
+    "9": "LABEL_9"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2,
+    "LABEL_3": 3,
+    "LABEL_4": 4,
+    "LABEL_5": 5,
+    "LABEL_6": 6,
+    "LABEL_7": 7,
+    "LABEL_8": 8,
+    "LABEL_9": 9
+  },
+  "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",
+  "torch_dtype": "float32",
+  "transformers_version": "4.48.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a2beb3b76e98176ea3d546ad6151523d2fbd372ebe9d123405823e523916ffa8
+size 438750416

modeling.py

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+from transformers import BertForSequenceClassification, AutoConfig, AutoTokenizer
+import torch.nn as nn
+
+class CustomBertForSequenceClassification(BertForSequenceClassification):
+    def __init__(self, config):
+        super().__init__(config)
+        # Replace the default classifier (single linear layer) with a Sequential head
+        self.classifier = nn.Sequential(
+            nn.Linear(config.hidden_size, config.custom_head_hidden_size),
+            nn.ReLU(),
+            nn.Linear(config.custom_head_hidden_size, config.num_labels)
+        )