vjawa_test_readme_updates (#5)

- Add Readme.md to model card (10349c9657f59153f0cb3736dd709100b9d861f0)
- Add Readme.md to model card (1dca7875e59127b553ecde21e4c7bcce549d0b2e)


Co-authored-by: Vibhu Jawa <VibhuJawa@users.noreply.huggingface.co>

README.md

@@ -3,7 +3,46 @@ tags:
 - pytorch_model_hub_mixin
 - model_hub_mixin
 ---
+# nvidia/domain-classifier
 
-This model has been pushed to the Hub using ****:
-- Repo: [More Information Needed]
-- Docs: [More Information Needed]
+This repository contains the code for the domain classifier model.
+
+# How to use in transformers
+To use the Domain classifier, use the following code:
+
+```python3
+
+import torch
+from torch import nn
+from transformers import AutoModel, AutoTokenizer, AutoConfig
+from huggingface_hub import PyTorchModelHubMixin
+
+class CustomModel(nn.Module, PyTorchModelHubMixin):
+    def __init__(self, config):
+        super(CustomModel, self).__init__()
+        self.model = AutoModel.from_pretrained(config['base_model'])
+        self.dropout = nn.Dropout(config['fc_dropout'])
+        self.fc = nn.Linear(self.model.config.hidden_size, len(config['id2label']))
+
+    def forward(self, input_ids, attention_mask):
+        features = self.model(input_ids=input_ids, attention_mask=attention_mask).last_hidden_state
+        dropped = self.dropout(features)
+        outputs = self.fc(dropped)
+        return torch.softmax(outputs[:, 0, :], dim=1)
+
+# Setup configuration and model
+config = AutoConfig.from_pretrained("nvidia/domain-classifier")
+tokenizer = AutoTokenizer.from_pretrained("nvidia/domain-classifier")
+model = CustomModel.from_pretrained("nvidia/domain-classifier")
+
+# Prepare and process inputs
+text_samples = ["Sports is a popular domain", "Politics is a popular domain"]
+inputs = tokenizer(text_samples, return_tensors="pt", padding="longest", truncation=True)
+outputs = model(inputs['input_ids'], inputs['attention_mask'])
+
+# Predict and display results
+predicted_classes = torch.argmax(outputs, dim=1)
+predicted_domains = [config.id2label[class_idx.item()] for class_idx in predicted_classes.cpu().numpy()]
+print(predicted_domains)
+# ['Sports', 'News']
+```

config.json

@@ -1,5 +1,6 @@
 {
   "base_model": "microsoft/deberta-v3-base",
+  "model_type": "deberta-v2",
   "config_path": null,
   "fc_dropout": 0.2,
   "id2label": {