Update README.md

README.md

@@ -22,13 +22,24 @@ This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://hugg
 It achieves the following results on the evaluation set:
 - Loss: 0.0917
 - Accuracy: 0.9835
-- Far: 0.0068
-- Frr: 0.0330
-- Eer: 0.0199
+- FAR: 0.0068
+- FRR: 0.0330
+- EER: 0.0199
 
 ## Model description
 
-More information needed
+### Quick Use
+
+```python
+  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 +66,7 @@ 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    |
 |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------:|:------:|
 | 0.6363        | 0.39  | 2500  | 0.4678          | 0.8652   | 0.0178 | 0.3326 | 0.1752 |
 | 0.2896        | 0.79  | 5000  | 0.1145          | 0.9744   | 0.0170 | 0.0402 | 0.0286 |