Update README.md with model details, usage code, and download instructions

README.md

@@ -23,22 +23,6 @@ license: mit
 
 This model is a fine-tuned version of DistilHuBERT for audio genre classification tasks. DistilHuBERT is a distilled variant of the HuBERT model, optimized for efficient and effective audio processing. This classifier is capable of categorizing audio files into various musical genres, leveraging the powerful representations learned by DistilHuBERT.
 
-## Trigger words
-
-You should use `DistilHuBERT` to trigger the image generation.
-
-You should use `Audio Encoder` to trigger the image generation.
-
-You should use `Transfer Learning` to trigger the image generation.
-
-
-## Download model
-
-Weights for this model are available in Safetensors,PyTorch format.
-
-[Download](/danilotpnta/HuBERT-Genre-Clf/tree/main) them in the Files & versions tab.
-
-
 
 ## Model Details:
 
@@ -80,6 +64,11 @@ print(f"Predicted genre: {model.config.id2label[predicted_class]}")
 
 The model achieves an impressive **80.63%** accuracy on the [GTZAN test dataset](https://www.kaggle.com/andradaolteanu/gtzan-dataset-music-genre-classification) for genre classification tasks, demonstrating its efficacy and reliability. This high level of performance makes it a valuable asset for various applications, including music recommendation systems and audio analysis tools. 
 
+## Download model
+
+Weights for this model are available in Safetensors,PyTorch format.
+
+[Download](/danilotpnta/HuBERT-Genre-Clf/tree/main) them in the Files & versions tab.
 
 **License: MIT**