update with context

README.md

@@ -1,7 +1,9 @@
 ---
 license: apache-2.0
 tags:
-- generated_from_trainer
+- music
+- genre
+- classification
 datasets:
 - marsyas/gtzan
 metrics:
@@ -9,6 +11,8 @@ metrics:
 model-index:
 - name: distilhubert-finetuned-gtzan_accuracy_93
   results: []
+language:
+- en
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -16,26 +20,25 @@ should probably proofread and complete it, then remove this comment. -->
 
 # distilhubert-finetuned-gtzan_accuracy_93
 
-This model is a fine-tuned version of [yuval6967/distilhubert-finetuned-gtzan](https://huggingface.co/yuval6967/distilhubert-finetuned-gtzan) on the GTZAN dataset.
+### This model is a fine-tuned version of [yuval6967/distilhubert-finetuned-gtzan](https://huggingface.co/yuval6967/distilhubert-finetuned-gtzan) on the GTZAN dataset.
 It achieves the following results on the evaluation set:
 - Loss: 0.5121
 - __Accuracy: 0.93__
 
 ## Model description
 
-Fine-tuned model to demonstrate > 87% accuracy for the Huggingface Audio course 
+- Fine-tuned model to demonstrate > 87% accuracy for the [Huggingface Audio course](https://huggingface.co/learn/audio-course/chapter0/introduction) 
 
 ## Intended uses & limitations
 
-- Example fine-tuned model for HF Audio course credit
+- Model is built to identify the genre of music based on a ~30 sec clip
 
 ## Training and evaluation data
 
 More information needed
 
 ## Training procedure
-- a non-standard split of 0.20 for test_size was used
-  - this model is built on top of an existing model [yuval6967/distilhubert-finetuned-gtzan] that had an accuracy of 87% from 
+- test_size = 0.20 was used for the split 
 
 ### Training hyperparameters
 
@@ -70,4 +73,4 @@ The following hyperparameters were used during training:
 - Transformers 4.30.2
 - Pytorch 2.0.1+cu118
 - Datasets 2.13.1
-- Tokenizers 0.13.3
+- Tokenizers 0.13.3