Upload model with LFS tracking

config.json

@@ -0,0 +1,25 @@
+{
+  "architectures": [
+    "BertRegressionModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "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",
+  "torch_dtype": "float32",
+  "transformers_version": "4.53.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c467472adfb17f9cde1163d0822354399bc73151590d41df15a088ae0a4ddbf9
+size 437955572

modelling_bert_regression.py

@@ -0,0 +1,37 @@
+import torch
+import torch.nn as nn
+from transformers import BertTokenizer, BertModel, BertPreTrainedModel, AutoModel
+
+# Configuration
+MODEL_NAME = "bert-base-uncased"
+MAX_LENGTH = 128
+BATCH_SIZE = 16
+NUM_EPOCHS = 3
+LEARNING_RATE = 2e-5
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+tokenizer = BertTokenizer.from_pretrained(MODEL_NAME)
+
+def preprocess(arguments):
+    encoding = tokenizer(arguments, truncation=True, padding="max_length", max_length=MAX_LENGTH)
+    return encoding
+
+class BertRegressionModel(BertPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.bert = BertModel(config)
+        self.regressor = nn.Sequential(
+            nn.Dropout(config.hidden_dropout_prob),
+            nn.Linear(config.hidden_size, 1),
+            nn.Sigmoid()  # ensures output is in [0, 1]
+        )
+        self.init_weights()
+
+    def forward(self, input_ids, attention_mask, labels=None):
+        outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)
+        pooled_output = outputs.pooler_output
+        preds = self.regressor(pooled_output).squeeze(-1)
+        return preds
+
+AutoModel.register(BertRegressionModel)