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config.json

@@ -1,4 +1,11 @@
 {
+  "architectures": [
+    "BertModelForESGClassification"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_ESGBertReddit.ESGRedditConfig",
+    "AutoModelForSequenceClassification": "modeling_ESGBertReddit.BertModelForESGClassification"
+  },
   "id2label": {
     "0": "D",
     "1": "C",
@@ -13,5 +20,6 @@
   },
   "model_type": "ESGBertReddit",
   "num_classes": 4,
+  "torch_dtype": "float32",
   "transformers_version": "4.24.0"
 }

configuration_ESGBertReddit.py

@@ -0,0 +1,13 @@
+from transformers import PretrainedConfig
+class ESGRedditConfig(PretrainedConfig):
+    model_type = "ESGBertReddit"
+
+    def __init__(
+        self,
+        architectures = ["BertForSequenceClassification"],
+        num_classes: int = 4,
+        **kwargs
+    ):  
+        self.architectures = architectures
+        self.num_classes = num_classes
+        super().__init__(**kwargs)

modeling_ESGBertReddit.py

@@ -0,0 +1,34 @@
+import torch
+import torch.nn as nn
+from transformers import PreTrainedModel,BertModel
+from ESGBertReddit_model.configuration_ESGBertReddit import ESGRedditConfig
+class ClassificationModel(PreTrainedModel):
+    config_class = ESGRedditConfig
+
+    def __init__(self,config):
+        super().__init__(config)
+        self.bert = BertModel.from_pretrained('yiyanghkust/finbert-esg',output_attentions=True)
+        self.W = nn.Linear(self.bert.config.hidden_size, config.num_classes)
+        self.num_classes = config.num_classes
+        
+    def forward(self, input_ids, attention_mask, token_type_ids):
+        h, _, attn = self.bert(input_ids=input_ids, 
+                               attention_mask=attention_mask,
+                               token_type_ids=token_type_ids).values()
+        h_cls = h[:,0,:]
+        output = self.W(h_cls)
+        return output, attn
+
+class BertModelForESGClassification(PreTrainedModel):
+    config_class = ESGRedditConfig
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = ClassificationModel(config)
+
+    def forward(self, inputs, labels=None):
+        logits,_ = self.model(**inputs)
+        if labels is not None:
+            loss = torch.nn.cross_entropy(logits, labels)
+            return {"loss": loss, "logits": logits}
+        return {"logits": logits}

pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3cfd0f21b11a64ad7bcaa71932f94a52b5fb2feb5efeb4b50f35f6dda8d9a81e
+size 439088877