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--- |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: MIDICausalFinetuning2 |
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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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# MIDICausalFinetuning2 |
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This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6756 |
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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: 2e-05 |
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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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- num_epochs: 100 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| No log | 1.0 | 9 | 7.7655 | |
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| No log | 2.0 | 18 | 6.4257 | |
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| No log | 3.0 | 27 | 5.4697 | |
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| No log | 4.0 | 36 | 4.9705 | |
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| No log | 5.0 | 45 | 4.7258 | |
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| No log | 6.0 | 54 | 4.5740 | |
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| No log | 7.0 | 63 | 4.4554 | |
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| No log | 8.0 | 72 | 4.3483 | |
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| No log | 9.0 | 81 | 4.2406 | |
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| No log | 10.0 | 90 | 4.1217 | |
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| No log | 11.0 | 99 | 3.9690 | |
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| No log | 12.0 | 108 | 3.7765 | |
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| No log | 13.0 | 117 | 3.6364 | |
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| No log | 14.0 | 126 | 3.5090 | |
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| No log | 15.0 | 135 | 3.4009 | |
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| No log | 16.0 | 144 | 3.2948 | |
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| No log | 17.0 | 153 | 3.1934 | |
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| No log | 18.0 | 162 | 3.1031 | |
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| No log | 19.0 | 171 | 3.0232 | |
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| No log | 20.0 | 180 | 2.9464 | |
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| No log | 21.0 | 189 | 2.8734 | |
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| No log | 22.0 | 198 | 2.8016 | |
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| No log | 23.0 | 207 | 2.7296 | |
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| No log | 24.0 | 216 | 2.6571 | |
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| No log | 25.0 | 225 | 2.5846 | |
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| No log | 26.0 | 234 | 2.5193 | |
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| No log | 27.0 | 243 | 2.4498 | |
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| No log | 28.0 | 252 | 2.3844 | |
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| No log | 29.0 | 261 | 2.3150 | |
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| No log | 30.0 | 270 | 2.2558 | |
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| No log | 31.0 | 279 | 2.1873 | |
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| No log | 32.0 | 288 | 2.1213 | |
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| No log | 33.0 | 297 | 2.0649 | |
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| No log | 34.0 | 306 | 1.9997 | |
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| No log | 35.0 | 315 | 1.9421 | |
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| No log | 36.0 | 324 | 1.8803 | |
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| No log | 37.0 | 333 | 1.8131 | |
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| No log | 38.0 | 342 | 1.7380 | |
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| No log | 39.0 | 351 | 1.6847 | |
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| No log | 40.0 | 360 | 1.5993 | |
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| No log | 41.0 | 369 | 1.5855 | |
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| No log | 42.0 | 378 | 1.5034 | |
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| No log | 43.0 | 387 | 1.4867 | |
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| No log | 44.0 | 396 | 1.4380 | |
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| No log | 45.0 | 405 | 1.4309 | |
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| No log | 46.0 | 414 | 1.3585 | |
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| No log | 47.0 | 423 | 1.3231 | |
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| No log | 48.0 | 432 | 1.3071 | |
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| No log | 49.0 | 441 | 1.2690 | |
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| No log | 50.0 | 450 | 1.2417 | |
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| No log | 51.0 | 459 | 1.2078 | |
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| No log | 52.0 | 468 | 1.1709 | |
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| No log | 53.0 | 477 | 1.1457 | |
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| No log | 54.0 | 486 | 1.1317 | |
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| No log | 55.0 | 495 | 1.1155 | |
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| 2.8999 | 56.0 | 504 | 1.0914 | |
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| 2.8999 | 57.0 | 513 | 1.0625 | |
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| 2.8999 | 58.0 | 522 | 1.0380 | |
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| 2.8999 | 59.0 | 531 | 1.0190 | |
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| 2.8999 | 60.0 | 540 | 0.9976 | |
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| 2.8999 | 61.0 | 549 | 0.9716 | |
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| 2.8999 | 62.0 | 558 | 0.9544 | |
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| 2.8999 | 63.0 | 567 | 0.9289 | |
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| 2.8999 | 64.0 | 576 | 0.9157 | |
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| 2.8999 | 65.0 | 585 | 0.8983 | |
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| 2.8999 | 66.0 | 594 | 0.8923 | |
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| 2.8999 | 67.0 | 603 | 0.8751 | |
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| 2.8999 | 68.0 | 612 | 0.8684 | |
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| 2.8999 | 69.0 | 621 | 0.8485 | |
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| 2.8999 | 70.0 | 630 | 0.8349 | |
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| 2.8999 | 71.0 | 639 | 0.8261 | |
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| 2.8999 | 72.0 | 648 | 0.8072 | |
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| 2.8999 | 73.0 | 657 | 0.8034 | |
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| 2.8999 | 74.0 | 666 | 0.7947 | |
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| 2.8999 | 75.0 | 675 | 0.7787 | |
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| 2.8999 | 76.0 | 684 | 0.7700 | |
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| 2.8999 | 77.0 | 693 | 0.7581 | |
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| 2.8999 | 78.0 | 702 | 0.7577 | |
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| 2.8999 | 79.0 | 711 | 0.7472 | |
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| 2.8999 | 80.0 | 720 | 0.7514 | |
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| 2.8999 | 81.0 | 729 | 0.7317 | |
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| 2.8999 | 82.0 | 738 | 0.7334 | |
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| 2.8999 | 83.0 | 747 | 0.7233 | |
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| 2.8999 | 84.0 | 756 | 0.7148 | |
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| 2.8999 | 85.0 | 765 | 0.7139 | |
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| 2.8999 | 86.0 | 774 | 0.7048 | |
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| 2.8999 | 87.0 | 783 | 0.7033 | |
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| 2.8999 | 88.0 | 792 | 0.6972 | |
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| 2.8999 | 89.0 | 801 | 0.6946 | |
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| 2.8999 | 90.0 | 810 | 0.6899 | |
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| 2.8999 | 91.0 | 819 | 0.6867 | |
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| 2.8999 | 92.0 | 828 | 0.6852 | |
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| 2.8999 | 93.0 | 837 | 0.6855 | |
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| 2.8999 | 94.0 | 846 | 0.6815 | |
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| 2.8999 | 95.0 | 855 | 0.6793 | |
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| 2.8999 | 96.0 | 864 | 0.6782 | |
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| 2.8999 | 97.0 | 873 | 0.6754 | |
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| 2.8999 | 98.0 | 882 | 0.6763 | |
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| 2.8999 | 99.0 | 891 | 0.6758 | |
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| 2.8999 | 100.0 | 900 | 0.6756 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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