Remove automodel as it was not working

modelling_bert_regression.py

@@ -1,37 +0,0 @@
-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)