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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: PromoGen_K562_2080Ti_restart |
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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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# PromoGen_K562_2080Ti_restart |
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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: 0.4624 |
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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: 0.0005 |
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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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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 64 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 1000 |
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- num_epochs: 25 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:------:|:---------------:| |
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| 0.7676 | 0.49 | 2500 | 0.7383 | |
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| 0.7121 | 0.97 | 5000 | 0.6867 | |
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| 0.6914 | 1.46 | 7500 | 0.6705 | |
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| 0.6837 | 1.95 | 10000 | 0.6622 | |
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| 0.6778 | 2.44 | 12500 | 0.6558 | |
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| 0.6748 | 2.92 | 15000 | 0.6517 | |
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| 0.6676 | 3.41 | 17500 | 0.6433 | |
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| 0.6593 | 3.9 | 20000 | 0.6358 | |
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| 0.6584 | 4.38 | 22500 | 0.6320 | |
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| 0.6557 | 4.87 | 25000 | 0.6301 | |
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| 0.6523 | 5.36 | 27500 | 0.6257 | |
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| 0.6478 | 5.84 | 30000 | 0.6236 | |
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| 0.6393 | 6.33 | 32500 | 0.6145 | |
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| 0.6039 | 6.82 | 35000 | 0.5658 | |
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| 0.5616 | 7.31 | 37500 | 0.5376 | |
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| 0.5518 | 7.79 | 40000 | 0.5310 | |
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| 0.5509 | 8.28 | 42500 | 0.5273 | |
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| 0.5487 | 8.77 | 45000 | 0.5261 | |
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| 0.5479 | 9.25 | 47500 | 0.5249 | |
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| 0.546 | 9.74 | 50000 | 0.5242 | |
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| 0.5447 | 10.23 | 52500 | 0.5229 | |
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| 0.5439 | 10.71 | 55000 | 0.5220 | |
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| 0.5433 | 11.2 | 57500 | 0.5209 | |
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| 0.5394 | 11.69 | 60000 | 0.5162 | |
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| 0.5153 | 12.18 | 62500 | 0.4944 | |
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| 0.5137 | 12.66 | 65000 | 0.4932 | |
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| 0.514 | 13.15 | 67500 | 0.4924 | |
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| 0.5131 | 13.64 | 70000 | 0.4919 | |
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| 0.5104 | 14.12 | 72500 | 0.4914 | |
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| 0.5122 | 14.61 | 75000 | 0.4906 | |
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| 0.5089 | 15.1 | 77500 | 0.4901 | |
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| 0.5076 | 15.59 | 80000 | 0.4891 | |
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| 0.4986 | 16.07 | 82500 | 0.4721 | |
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| 0.4875 | 16.56 | 85000 | 0.4672 | |
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| 0.4887 | 17.05 | 87500 | 0.4669 | |
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| 0.4839 | 17.53 | 90000 | 0.4661 | |
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| 0.4849 | 18.02 | 92500 | 0.4654 | |
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| 0.4848 | 18.51 | 95000 | 0.4649 | |
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| 0.4831 | 18.99 | 97500 | 0.4646 | |
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| 0.4816 | 19.48 | 100000 | 0.4644 | |
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| 0.4808 | 19.97 | 102500 | 0.4637 | |
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| 0.4812 | 20.46 | 105000 | 0.4634 | |
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| 0.4813 | 20.94 | 107500 | 0.4633 | |
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| 0.4818 | 21.43 | 110000 | 0.4631 | |
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| 0.4813 | 21.92 | 112500 | 0.4629 | |
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| 0.4782 | 22.4 | 115000 | 0.4628 | |
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| 0.4804 | 22.89 | 117500 | 0.4626 | |
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| 0.4815 | 23.38 | 120000 | 0.4625 | |
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| 0.4812 | 23.87 | 122500 | 0.4625 | |
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| 0.4785 | 24.35 | 125000 | 0.4624 | |
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| 0.4795 | 24.84 | 127500 | 0.4624 | |
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### Framework versions |
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- Transformers 4.24.0 |
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- Pytorch 1.13.0 |
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- Datasets 2.7.0 |
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- Tokenizers 0.13.0.dev0 |
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