Upload DistillBERTClassClarity

config.json

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+{
+  "architectures": [
+    "DistillBERTClassClarity"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_essay_clarity.DistillBERTClassClarityConfig",
+    "AutoModel": "modeling_essay_clarity.DistillBERTClassClarity"
+  },
+  "model_type": "distill_bert_clarity",
+  "torch_dtype": "float32",
+  "transformers_version": "4.24.0"
+}

configuration_essay_clarity.py

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+from transformers import PretrainedConfig
+
+
+class DistillBERTClassClarityConfig(PretrainedConfig):
+    model_type = "distill_bert_clarity"
+
+    def __init__(
+            self,
+            **kwargs,
+    ):
+        super().__init__(**kwargs)

modeling_essay_clarity.py

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+# Creating the customized model, by adding a drop out and a dense layer on top of distil bert to get the final output for the model.
+# Importing the libraries needed
+import torch
+import transformers
+from torch.utils.data import Dataset, DataLoader
+from transformers import DistilBertModel, DistilBertTokenizer, PreTrainedModel
+from configuration_essay_clarity import DistillBERTClassClarityConfig
+
+
+class DistillBERTClassClarity(PreTrainedModel):
+    config_class = DistillBERTClassClarityConfig
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.l1 = DistilBertModel.from_pretrained("distilbert-base-uncased")
+        self.pre_classifier = torch.nn.Linear(768, 768)
+        self.dropout = torch.nn.Dropout(0.3)
+        self.classifier = torch.nn.Linear(768, 1)
+        # https://glassboxmedicine.com/2019/05/26/classification-sigmoid-vs-softmax/
+        # self.softmax = torch.nn.Softmax(dim=1)
+        # self.sigmoid = torch.nn.Sigmoid() # apply sigmoid on vector of 1*4
+
+    def forward(self, ids=None, mask=None):
+        output_1 = self.l1(input_ids=ids, attention_mask=mask)
+        hidden_state = output_1[0]
+        pooler = hidden_state[:, 0]
+        pooler = self.pre_classifier(pooler)
+        pooler = torch.nn.ReLU()(pooler)
+        pooler = self.dropout(pooler)
+        output = self.classifier(pooler)
+        # output = self.sigmoid(output)
+        return output

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:50b5288e6f41cfb080d462347bd811f822d491995317cb24d5f90e1b00767a9d
+size 267850221