Create README.md

README.md

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+Pretrained model based on [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) with further mathematical pre-training.
+
+Compared to deberta-v3-base, 300 additional mathematical LaTeX tokens have been added before the mathematical pre-training. As this additional pre-training used NSP-like tasks, a pooling layer has been added to the model (`bias` and `weight`). If you don't need this pooling layer, just use the standard transformers DeBERTa model. If you want to use the additional pooling layer like the BERT one, a wrapper class like the following may be used:
+```python
+from typing import Mapping, Any
+
+import torch
+from torch import nn
+from transformers import DebertaV2Model, DebertaV2Tokenizer, AutoConfig, AutoTokenizer
+
+class DebertaV2ModelWithPoolingLayer:
+
+    def __init__(self, pretrained_model_name):
+        super(DebertaV2ModelWithPoolingLayer, self).__init__()
+
+        # Load the Deberta model and tokenizer
+        self.deberta = DebertaV2Model.from_pretrained(pretrained_model_name)
+        self.tokenizer = DebertaV2Tokenizer.from_pretrained(pretrained_model_name)
+
+        # Add a pooling layer (Linear + tanh activation) for the CLS token
+        self.pooling_layer = nn.Sequential(
+            nn.Linear(self.deberta.config.hidden_size, self.deberta.config.hidden_size),
+            nn.Tanh()
+        )
+
+        self.config = self.deberta.config
+        self.embeddings = self.deberta.embeddings
+
+
+    def forward(self, input_ids, attention_mask, *args, **kwargs):
+        # Forward pass through the Deberta model
+        outputs = self.deberta(input_ids, attention_mask=attention_mask, *args, **kwargs)
+
+        # Extract the hidden states from the output
+        hidden_states = outputs.last_hidden_state
+
+        # Get the CLS token representation (first token)
+        cls_token = hidden_states[:, 0, :]
+
+        # Apply the pooling layer to the CLS token representation
+        pooled_output = self.pooling_layer(cls_token)
+        # Include the pooled_output in the output dictionary as 'pooling_layer'
+        outputs["pooler_output"] = pooled_output
+
+        return outputs
+
+    def save_pretrained(self, path):
+        # Save the model's state_dict, configuration, and tokenizer
+        state_dict = self.deberta.state_dict()
+        state_dict.update(self.pooling_layer[0].state_dict())
+
+        torch.save(state_dict, f"{path}/pytorch_model.bin")
+        self.deberta.config.save_pretrained(path)
+        self.tokenizer.save_pretrained(path)
+
+    def load_state_dict(self, state_dict: Mapping[str, Any], strict: bool = True):
+        pooler_keys = ['bias', 'weight']
+        deberta_state_dict = {k: v for k, v in state_dict.items() if k not in pooler_keys}
+        pooler_state_dict = {k: v for k, v in state_dict.items() if k in pooler_keys}
+        self.deberta.load_state_dict(deberta_state_dict, strict=strict)
+        self.pooling_layer[0].load_state_dict(pooler_state_dict)
+
+    @classmethod
+    def from_pretrained(cls, name):
+        # Initialize the instance
+        instance = cls(name)
+
+        try:
+            # Load the model's state_dict
+            instance.load_state_dict(torch.load(f"{name}/pytorch_model.bin"))
+        except FileNotFoundError:
+            print("Could not find DeBERTa pooling layer. Initialize new values")
+
+        # Load the configuration and tokenizer
+        instance.deberta.config = AutoConfig.from_pretrained(name)
+        instance.tokenizer = AutoTokenizer.from_pretrained(name)
+
+        return instance
+```