Create README.md

README.md

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+---
+datasets:
+- marsyas/gtzan
+metrics:
+- accuracy
+pipeline_tag: audio-classification
+tags:
+- music
+- audio
+---
+
+# Description
+
+This model is a specialized version of the <b>distilhubert</b> model fine-tuned on the <b>gtzan</b> dataset for the task of Music Genre Classification.
+
+## Development
+- Kaggle Notebook: [Audio Data: Music Genre Classification](https://www.kaggle.com/code/lusfernandotorres/audio-data-music-genre-classification)
+
+
+## Training Parameters
+```python
+evaluation_strategy = 'epoch',
+save_strategy = 'epoch',
+load_best_model_at_end = True,
+metric_for_best_model = 'accuracy',
+learning_rate = 5e-5,
+seed = 42,
+per_device_train_batch_size = 8,
+per_device_eval_batch_size = 8,
+gradient_accumulation_steps = 1,
+num_train_epochs = 15,
+warmup_ratio = 0.1,
+fp16 = True,
+save_total_limit = 2,
+report_to = 'none'
+```
+
+## Training and Validation Results
+
+```python
+Epoch	Training Loss	Validation Loss	Accuracy
+1	      No log	        2.050576	0.395000
+2	      No log	        1.387915	0.565000
+3	      No log	        1.141497	0.665000
+4	      No log	        1.052763	0.675000
+5	      1.354600	        0.846402	0.745000
+6	      1.354600	        0.858698	0.750000
+7	      1.354600	        0.864531	0.730000
+8	      1.354600	        0.765039	0.775000
+9	      1.354600	        0.790847	0.785000
+10	      0.250100	        0.873926	0.785000
+11	      0.250100	        0.928275	0.770000
+12	      0.250100	        0.851429	0.780000
+13	      0.250100	        0.922214	0.770000
+14	      0.250100	        0.916481	0.780000
+15	      0.028000	        0.946075	0.770000
+TrainOutput(global_step=1500, training_loss=0.5442592652638754,
+metrics={'train_runtime': 12274.2966, 'train_samples_per_second': 0.976,
+'train_steps_per_second': 0.122, 'total_flos': 8.177513845536e+17, 'train_loss': 0.5442592652638754, 'epoch': 15.0})
+```
+
+## Reference
+This model is based on the original <b>HuBERT</b> architecture, as detailed in:
+
+Hsu et al. (2021). HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units. [arXiv:2106.07447](https://arxiv.org/pdf/2106.07447.pdf)