Update README.md

README.md

@@ -11,6 +11,8 @@ metrics:
 model-index:
 - name: hubert-base-960h-itw-deepfake
   results: []
+language:
+- en
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -22,13 +24,24 @@ This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggi
 It achieves the following results on the evaluation set:
 - Loss: 0.0756
 - Accuracy: 0.9873
-- Far: 0.0083
-- Frr: 0.0203
-- Eer: 0.0143
+- FAR: 0.0083
+- FRR: 0.0203
+- EER: 0.0143
 
 ## Model description
 
-More information needed
+### Quick Use
+
+```py3
+  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+  config = AutoConfig.from_pretrained("abhishtagatya/hubert-base-960h-itw-deepfake")
+  feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("abhishtagatya/hubert-base-960h-itw-deepfake")
+
+  model = HubertForSequenceClassification.from_pretrained("abhishtagatya/hubert-base-960h-itw-deepfake", config=config,).to(device)
+
+  # Your Logic Here
+```
 
 ## Intended uses & limitations
 
@@ -55,7 +68,7 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Far    | Frr    | Eer    |
+| Training Loss | Epoch | Step  | Validation Loss | Accuracy | FAR    | FRR    | EER    |
 |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------:|:------:|
 | 0.4081        | 0.39  | 2500  | 0.1152          | 0.9722   | 0.0285 | 0.0267 | 0.0276 |
 | 0.1168        | 0.79  | 5000  | 0.0822          | 0.9844   | 0.0120 | 0.0216 | 0.0168 |
@@ -69,4 +82,4 @@ The following hyperparameters were used during training:
 - Transformers 4.38.0.dev0
 - Pytorch 2.1.2+cu121
 - Datasets 2.16.2.dev0
-- Tokenizers 0.15.1
+- Tokenizers 0.15.1