End of training

README.md

@@ -0,0 +1,98 @@
+---
+license: apache-2.0
+base_model: ntu-spml/distilhubert
+tags:
+- generated_from_trainer
+datasets:
+- marsyas/gtzan
+metrics:
+- accuracy
+model-index:
+- name: distilhubert-finetuned-gtzan
+  results:
+  - task:
+      name: Audio Classification
+      type: audio-classification
+    dataset:
+      name: GTZAN
+      type: marsyas/gtzan
+      config: all
+      split: train
+      args: all
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.8
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# distilhubert-finetuned-gtzan
+
+This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.8614
+- Accuracy: 0.8
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 16
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 20
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 2.2268        | 0.99  | 56   | 2.1858          | 0.48     |
+| 1.7472        | 2.0   | 113  | 1.6259          | 0.58     |
+| 1.3293        | 2.99  | 169  | 1.1815          | 0.72     |
+| 1.0368        | 4.0   | 226  | 1.0176          | 0.69     |
+| 0.8106        | 4.99  | 282  | 0.8129          | 0.76     |
+| 0.5371        | 6.0   | 339  | 0.8296          | 0.72     |
+| 0.6545        | 6.99  | 395  | 0.7186          | 0.77     |
+| 0.4676        | 8.0   | 452  | 0.6627          | 0.76     |
+| 0.2729        | 8.99  | 508  | 0.5993          | 0.84     |
+| 0.2113        | 10.0  | 565  | 0.6360          | 0.8      |
+| 0.1475        | 10.99 | 621  | 0.6244          | 0.78     |
+| 0.0616        | 12.0  | 678  | 0.6762          | 0.83     |
+| 0.0429        | 12.99 | 734  | 0.7241          | 0.82     |
+| 0.0259        | 14.0  | 791  | 0.7547          | 0.82     |
+| 0.0207        | 14.99 | 847  | 0.7636          | 0.82     |
+| 0.0179        | 16.0  | 904  | 0.7817          | 0.82     |
+| 0.0304        | 16.99 | 960  | 0.7976          | 0.81     |
+| 0.0146        | 18.0  | 1017 | 0.8193          | 0.81     |
+| 0.0135        | 18.99 | 1073 | 0.8402          | 0.8      |
+| 0.0136        | 19.82 | 1120 | 0.8614          | 0.8      |
+
+
+### Framework versions
+
+- Transformers 4.35.2
+- Pytorch 2.1.0+cu121
+- Datasets 2.16.1
+- Tokenizers 0.15.0

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