End of training

README.md

@@ -0,0 +1,102 @@
+---
+license: apache-2.0
+base_model: ntu-spml/distilhubert
+tags:
+- generated_from_trainer
+datasets:
+- marsyas/gtzan
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+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: 0.7733333333333333
+    - name: Precision
+      type: precision
+      value: 0.775454513809777
+    - name: Recall
+      type: recall
+      value: 0.7733333333333333
+    - name: F1
+      type: f1
+      value: 0.7708532203254443
+---
+
+<!-- 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. -->
+
+[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/raspuntinov_ai/huggingface/runs/xti2wn9w)
+# 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: 0.7448
+- Accuracy: 0.7733
+- Precision: 0.7755
+- Recall: 0.7733
+- F1: 0.7709
+
+## 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
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 10
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
+| 2.0182        | 1.0   | 88   | 2.0020          | 0.3333   | 0.3990    | 0.3333 | 0.2547 |
+| 1.6019        | 2.0   | 176  | 1.4794          | 0.5333   | 0.6597    | 0.5333 | 0.4789 |
+| 1.0733        | 3.0   | 264  | 1.2329          | 0.6133   | 0.6930    | 0.6133 | 0.5993 |
+| 0.9451        | 4.0   | 352  | 1.1227          | 0.64     | 0.7214    | 0.64   | 0.6289 |
+| 0.9232        | 5.0   | 440  | 0.9426          | 0.7133   | 0.7398    | 0.7133 | 0.7071 |
+| 0.6552        | 6.0   | 528  | 0.8132          | 0.78     | 0.7795    | 0.78   | 0.7768 |
+| 0.4019        | 7.0   | 616  | 0.8478          | 0.7333   | 0.7428    | 0.7333 | 0.7285 |
+| 0.2836        | 8.0   | 704  | 0.7369          | 0.7933   | 0.8025    | 0.7933 | 0.7915 |
+| 0.207         | 9.0   | 792  | 0.7440          | 0.7933   | 0.7926    | 0.7933 | 0.7879 |
+| 0.3091        | 10.0  | 880  | 0.7448          | 0.7733   | 0.7755    | 0.7733 | 0.7709 |
+
+
+### Framework versions
+
+- Transformers 4.42.3
+- Pytorch 2.1.2
+- Datasets 2.20.0
+- Tokenizers 0.19.1

model.safetensors

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