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chord_model

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musiclang/musiclang-chord-v2-4k

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  1. README.md +42 -14
  2. model.safetensors +1 -1
  3. runs/Jan30_18-04-41_cd32c414008c/events.out.tfevents.1706637882.cd32c414008c.2129.7 +3 -0
  4. training_args.bin +1 -1
README.md CHANGED
@@ -13,7 +13,7 @@ should probably proofread and complete it, then remove this comment. -->
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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: 0.5219
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 0.0001
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- - train_batch_size: 32
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- - eval_batch_size: 32
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  - seed: 444
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: cosine_with_restarts
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss |
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- |:-------------:|:-----:|:----:|:---------------:|
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- | 0.9483 | 0.43 | 500 | 0.8710 |
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- | 0.7688 | 0.86 | 1000 | 0.7363 |
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- | 0.6949 | 1.28 | 1500 | 0.6479 |
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- | 0.6295 | 1.71 | 2000 | 0.5840 |
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- | 0.6093 | 2.14 | 2500 | 0.5532 |
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- | 0.5865 | 2.57 | 3000 | 0.5368 |
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- | 0.5559 | 2.99 | 3500 | 0.5281 |
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- | 0.5772 | 3.42 | 4000 | 0.5236 |
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- | 0.5641 | 3.85 | 4500 | 0.5219 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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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: 0.4598
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 0.0001
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+ - train_batch_size: 8
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+ - eval_batch_size: 8
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  - seed: 444
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: cosine_with_restarts
 
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss |
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+ |:-------------:|:-----:|:-----:|:---------------:|
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+ | 1.2264 | 0.11 | 500 | 1.1269 |
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+ | 0.9624 | 0.21 | 1000 | 0.9066 |
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+ | 0.8598 | 0.32 | 1500 | 0.8128 |
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+ | 0.8209 | 0.43 | 2000 | 0.7626 |
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+ | 0.7483 | 0.53 | 2500 | 0.7272 |
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+ | 0.7391 | 0.64 | 3000 | 0.7032 |
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+ | 0.7052 | 0.75 | 3500 | 0.6739 |
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+ | 0.6998 | 0.86 | 4000 | 0.6503 |
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+ | 0.6901 | 0.96 | 4500 | 0.6244 |
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+ | 0.6348 | 1.07 | 5000 | 0.6100 |
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+ | 0.654 | 1.18 | 5500 | 0.5891 |
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+ | 0.6227 | 1.28 | 6000 | 0.5765 |
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+ | 0.6148 | 1.39 | 6500 | 0.5624 |
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+ | 0.5973 | 1.5 | 7000 | 0.5538 |
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+ | 0.5853 | 1.6 | 7500 | 0.5441 |
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+ | 0.56 | 1.71 | 8000 | 0.5407 |
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+ | 0.574 | 1.82 | 8500 | 0.5342 |
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+ | 0.5589 | 1.92 | 9000 | 0.5296 |
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+ | 0.5634 | 2.03 | 9500 | 0.5254 |
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+ | 0.543 | 2.14 | 10000 | 0.5208 |
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+ | 0.5792 | 2.25 | 10500 | 0.5159 |
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+ | 0.5571 | 2.35 | 11000 | 0.5064 |
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+ | 0.5408 | 2.46 | 11500 | 0.4957 |
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+ | 0.5398 | 2.57 | 12000 | 0.4882 |
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+ | 0.537 | 2.67 | 12500 | 0.4834 |
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+ | 0.5512 | 2.78 | 13000 | 0.4786 |
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+ | 0.4842 | 2.89 | 13500 | 0.4753 |
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+ | 0.5275 | 2.99 | 14000 | 0.4721 |
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+ | 0.4899 | 3.1 | 14500 | 0.4710 |
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+ | 0.5222 | 3.21 | 15000 | 0.4666 |
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+ | 0.4929 | 3.31 | 15500 | 0.4645 |
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+ | 0.5049 | 3.42 | 16000 | 0.4631 |
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+ | 0.5002 | 3.53 | 16500 | 0.4613 |
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+ | 0.505 | 3.64 | 17000 | 0.4611 |
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+ | 0.507 | 3.74 | 17500 | 0.4602 |
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+ | 0.5169 | 3.85 | 18000 | 0.4598 |
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+ | 0.501 | 3.96 | 18500 | 0.4598 |
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  ### Framework versions
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