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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- marsyas/gtzan |
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metrics: |
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- accuracy |
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model-index: |
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- name: distilhubert-finetuned-gtzan |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# distilhubert-finetuned-gtzan |
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This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7554 |
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- Accuracy: 0.83 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-06 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 100 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:| |
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| 2.3168 | 1.0 | 113 | 2.2998 | 0.06 | |
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| 2.2869 | 2.0 | 226 | 2.2880 | 0.15 | |
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| 2.2711 | 3.0 | 339 | 2.2649 | 0.32 | |
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| 2.2407 | 4.0 | 452 | 2.2306 | 0.36 | |
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| 2.1993 | 5.0 | 565 | 2.1723 | 0.41 | |
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| 2.1239 | 6.0 | 678 | 2.0768 | 0.54 | |
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| 2.0001 | 7.0 | 791 | 1.9675 | 0.63 | |
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| 1.9217 | 8.0 | 904 | 1.8527 | 0.63 | |
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| 1.7955 | 9.0 | 1017 | 1.7538 | 0.62 | |
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| 1.8026 | 10.0 | 1130 | 1.6549 | 0.63 | |
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| 1.6166 | 11.0 | 1243 | 1.5623 | 0.68 | |
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| 1.5788 | 12.0 | 1356 | 1.4857 | 0.67 | |
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| 1.4279 | 13.0 | 1469 | 1.4114 | 0.64 | |
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| 1.3744 | 14.0 | 1582 | 1.3545 | 0.7 | |
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| 1.1651 | 15.0 | 1695 | 1.2848 | 0.67 | |
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| 1.1956 | 16.0 | 1808 | 1.2487 | 0.69 | |
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| 1.1852 | 17.0 | 1921 | 1.1826 | 0.73 | |
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| 1.1153 | 18.0 | 2034 | 1.1325 | 0.73 | |
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| 1.0218 | 19.0 | 2147 | 1.1174 | 0.71 | |
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| 1.0623 | 20.0 | 2260 | 1.0428 | 0.73 | |
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| 0.9385 | 21.0 | 2373 | 1.0111 | 0.75 | |
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| 0.9395 | 22.0 | 2486 | 0.9853 | 0.75 | |
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| 0.8799 | 23.0 | 2599 | 0.9868 | 0.72 | |
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| 0.74 | 24.0 | 2712 | 0.9136 | 0.76 | |
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| 0.8411 | 25.0 | 2825 | 0.9025 | 0.74 | |
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| 0.8784 | 26.0 | 2938 | 0.8757 | 0.76 | |
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| 0.726 | 27.0 | 3051 | 0.8720 | 0.75 | |
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| 0.7704 | 28.0 | 3164 | 0.8135 | 0.75 | |
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| 0.7628 | 29.0 | 3277 | 0.7514 | 0.82 | |
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| 0.6254 | 30.0 | 3390 | 0.7675 | 0.77 | |
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| 0.5432 | 31.0 | 3503 | 0.7689 | 0.77 | |
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| 0.5699 | 32.0 | 3616 | 0.7197 | 0.81 | |
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| 0.5448 | 33.0 | 3729 | 0.6838 | 0.82 | |
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| 0.5634 | 34.0 | 3842 | 0.7029 | 0.81 | |
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| 0.4702 | 35.0 | 3955 | 0.7170 | 0.77 | |
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| 0.3946 | 36.0 | 4068 | 0.6443 | 0.82 | |
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| 0.4749 | 37.0 | 4181 | 0.6318 | 0.83 | |
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| 0.317 | 38.0 | 4294 | 0.6420 | 0.83 | |
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| 0.3082 | 39.0 | 4407 | 0.6190 | 0.82 | |
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| 0.2932 | 40.0 | 4520 | 0.6196 | 0.83 | |
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| 0.2928 | 41.0 | 4633 | 0.6059 | 0.83 | |
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| 0.2902 | 42.0 | 4746 | 0.6290 | 0.82 | |
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| 0.3297 | 43.0 | 4859 | 0.6039 | 0.82 | |
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| 0.2645 | 44.0 | 4972 | 0.5924 | 0.83 | |
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| 0.2586 | 45.0 | 5085 | 0.6134 | 0.83 | |
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| 0.2815 | 46.0 | 5198 | 0.6237 | 0.81 | |
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| 0.3678 | 47.0 | 5311 | 0.6001 | 0.81 | |
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| 0.2904 | 48.0 | 5424 | 0.5742 | 0.84 | |
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| 0.1635 | 49.0 | 5537 | 0.6265 | 0.82 | |
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| 0.1144 | 50.0 | 5650 | 0.5945 | 0.81 | |
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| 0.1677 | 51.0 | 5763 | 0.5986 | 0.83 | |
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| 0.1879 | 52.0 | 5876 | 0.6099 | 0.83 | |
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| 0.1977 | 53.0 | 5989 | 0.5745 | 0.84 | |
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| 0.1255 | 54.0 | 6102 | 0.5959 | 0.82 | |
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| 0.0789 | 55.0 | 6215 | 0.6409 | 0.83 | |
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| 0.0634 | 56.0 | 6328 | 0.5985 | 0.82 | |
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| 0.1688 | 57.0 | 6441 | 0.5848 | 0.84 | |
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| 0.1464 | 58.0 | 6554 | 0.6173 | 0.83 | |
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| 0.1089 | 59.0 | 6667 | 0.6245 | 0.83 | |
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| 0.0963 | 60.0 | 6780 | 0.6343 | 0.82 | |
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| 0.0548 | 61.0 | 6893 | 0.6277 | 0.83 | |
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| 0.1293 | 62.0 | 7006 | 0.6128 | 0.83 | |
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| 0.0406 | 63.0 | 7119 | 0.6339 | 0.83 | |
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| 0.0532 | 64.0 | 7232 | 0.6480 | 0.83 | |
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| 0.214 | 65.0 | 7345 | 0.6661 | 0.81 | |
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| 0.1246 | 66.0 | 7458 | 0.6637 | 0.83 | |
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| 0.036 | 67.0 | 7571 | 0.6527 | 0.85 | |
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| 0.1168 | 68.0 | 7684 | 0.6517 | 0.84 | |
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| 0.0322 | 69.0 | 7797 | 0.6714 | 0.83 | |
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| 0.0362 | 70.0 | 7910 | 0.6912 | 0.81 | |
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| 0.1088 | 71.0 | 8023 | 0.6830 | 0.85 | |
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| 0.0258 | 72.0 | 8136 | 0.7039 | 0.82 | |
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| 0.0776 | 73.0 | 8249 | 0.6931 | 0.83 | |
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| 0.0684 | 74.0 | 8362 | 0.6688 | 0.82 | |
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| 0.0169 | 75.0 | 8475 | 0.6966 | 0.83 | |
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| 0.1039 | 76.0 | 8588 | 0.6914 | 0.83 | |
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| 0.0361 | 77.0 | 8701 | 0.6978 | 0.84 | |
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| 0.0143 | 78.0 | 8814 | 0.7023 | 0.84 | |
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| 0.0161 | 79.0 | 8927 | 0.7156 | 0.83 | |
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| 0.0207 | 80.0 | 9040 | 0.7264 | 0.82 | |
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| 0.0129 | 81.0 | 9153 | 0.7155 | 0.82 | |
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| 0.0161 | 82.0 | 9266 | 0.7418 | 0.81 | |
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| 0.0284 | 83.0 | 9379 | 0.7270 | 0.82 | |
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| 0.0166 | 84.0 | 9492 | 0.7460 | 0.83 | |
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| 0.0144 | 85.0 | 9605 | 0.7430 | 0.83 | |
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| 0.0121 | 86.0 | 9718 | 0.7459 | 0.83 | |
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| 0.0116 | 87.0 | 9831 | 0.7579 | 0.83 | |
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| 0.0106 | 88.0 | 9944 | 0.7485 | 0.83 | |
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| 0.0104 | 89.0 | 10057 | 0.7586 | 0.81 | |
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| 0.0121 | 90.0 | 10170 | 0.7579 | 0.84 | |
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| 0.01 | 91.0 | 10283 | 0.7474 | 0.83 | |
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| 0.0099 | 92.0 | 10396 | 0.7528 | 0.83 | |
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| 0.0117 | 93.0 | 10509 | 0.7603 | 0.82 | |
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| 0.0174 | 94.0 | 10622 | 0.7646 | 0.83 | |
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| 0.0103 | 95.0 | 10735 | 0.7557 | 0.83 | |
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| 0.0102 | 96.0 | 10848 | 0.7548 | 0.83 | |
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| 0.0101 | 97.0 | 10961 | 0.7519 | 0.83 | |
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| 0.0096 | 98.0 | 11074 | 0.7557 | 0.83 | |
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| 0.0098 | 99.0 | 11187 | 0.7556 | 0.83 | |
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| 0.0098 | 100.0 | 11300 | 0.7554 | 0.83 | |
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### Framework versions |
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- Transformers 4.30.2 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.13.1 |
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- Tokenizers 0.13.3 |
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