Delete modeling_ESGify.py

modeling_ESGify.py

@@ -1,38 +0,0 @@
-from collections import OrderedDict
-from transformers import MPNetPreTrainedModel, MPNetModel
-from configuration_ESGify import ESGifyConfig
-import torch
-
-class ESGify(MPNetPreTrainedModel):
-    """Model for Classification ESG risks from text."""
-    config_class = ESGifyConfig
-
-    def __init__(self, config): #tuning only the head
-        super().__init__(config)
-        # Instantiate Parts of model
-        self.mpnet = MPNetModel(config,add_pooling_layer=False)
-        self.id2label =  config.id2label
-        self.label2id =  config.label2id
-        self.classifier = torch.nn.Sequential(OrderedDict([('norm',torch.nn.BatchNorm1d(768)),
-                                                ('linear',torch.nn.Linear(768,512)),
-                                                ('act',torch.nn.ReLU()),
-                                                ('batch_n',torch.nn.BatchNorm1d(512)),
-                                                ('drop_class', torch.nn.Dropout(0.2)),
-                                                ('class_l',torch.nn.Linear(512 ,47))]))
-
-    def mean_pooling(model_output, attention_mask):
-            token_embeddings = model_output #First element of model_output contains all token embeddings
-            input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
-            return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
- 
-    def forward(self, input_ids, attention_mask):
-         # Feed input to mpnet model
-        outputs = self.mpnet(input_ids=input_ids,
-                             attention_mask=attention_mask)
-         
-        # mean pooling dataset and eed input to classifier to compute logits
-        logits = self.classifier(self.mean_pooling(outputs['last_hidden_state'],attention_mask))
-         
-        # apply sigmoid
-        logits  = 1.0 / (1.0 + torch.exp(-logits))
-        return logits