out
This model is a fine-tuned version of /1TB_SSD/SB_AI/out_epoch1/out/checkpoint-1115000/ on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0645
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: 1
- eval_batch_size: 1
- seed: 2518227880
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
0.0867 | 0.07 | 75000 | 0.0742 |
0.0783 | 0.13 | 150000 | 0.0695 |
0.0719 | 0.2 | 225000 | 0.0732 |
0.0743 | 0.27 | 300000 | 0.0663 |
0.0659 | 0.34 | 375000 | 0.0686 |
0.0664 | 0.4 | 450000 | 0.0683 |
0.0637 | 0.47 | 525000 | 0.0680 |
0.0655 | 0.54 | 600000 | 0.0641 |
0.0676 | 0.6 | 675000 | 0.0644 |
0.0704 | 0.67 | 750000 | 0.0645 |
0.0687 | 0.74 | 825000 | 0.0610 |
0.059 | 0.81 | 900000 | 0.0652 |
0.0666 | 0.87 | 975000 | 0.0619 |
0.0624 | 0.94 | 1050000 | 0.0619 |
0.0625 | 1.01 | 1125000 | 0.0667 |
0.0614 | 1.03 | 1150000 | 0.0658 |
0.0597 | 1.05 | 1175000 | 0.0683 |
0.0629 | 1.07 | 1200000 | 0.0691 |
0.0603 | 1.1 | 1225000 | 0.0678 |
0.0601 | 1.12 | 1250000 | 0.0746 |
0.0606 | 1.14 | 1275000 | 0.0691 |
0.0671 | 1.16 | 1300000 | 0.0702 |
0.0625 | 1.19 | 1325000 | 0.0661 |
0.0617 | 1.21 | 1350000 | 0.0688 |
0.0579 | 1.23 | 1375000 | 0.0679 |
0.0663 | 1.25 | 1400000 | 0.0634 |
0.0583 | 1.28 | 1425000 | 0.0638 |
0.0623 | 1.3 | 1450000 | 0.0681 |
0.0615 | 1.32 | 1475000 | 0.0670 |
0.0592 | 1.34 | 1500000 | 0.0666 |
0.0626 | 1.37 | 1525000 | 0.0666 |
0.063 | 1.39 | 1550000 | 0.0647 |
0.0648 | 1.41 | 1575000 | 0.0653 |
0.0611 | 1.43 | 1600000 | 0.0700 |
0.0622 | 1.46 | 1625000 | 0.0634 |
0.0617 | 1.48 | 1650000 | 0.0651 |
0.0613 | 1.5 | 1675000 | 0.0634 |
0.0639 | 1.52 | 1700000 | 0.0661 |
0.0615 | 1.54 | 1725000 | 0.0644 |
0.0605 | 1.57 | 1750000 | 0.0662 |
0.0622 | 1.59 | 1775000 | 0.0656 |
0.0585 | 1.61 | 1800000 | 0.0633 |
0.0628 | 1.63 | 1825000 | 0.0625 |
0.0638 | 1.66 | 1850000 | 0.0662 |
0.0599 | 1.68 | 1875000 | 0.0664 |
0.0583 | 1.7 | 1900000 | 0.0668 |
0.0543 | 1.72 | 1925000 | 0.0631 |
0.06 | 1.75 | 1950000 | 0.0629 |
0.0615 | 1.77 | 1975000 | 0.0644 |
0.0587 | 1.79 | 2000000 | 0.0663 |
0.0647 | 1.81 | 2025000 | 0.0654 |
0.0604 | 1.84 | 2050000 | 0.0639 |
0.0641 | 1.86 | 2075000 | 0.0636 |
0.0604 | 1.88 | 2100000 | 0.0636 |
0.0654 | 1.9 | 2125000 | 0.0652 |
0.0588 | 1.93 | 2150000 | 0.0638 |
0.0616 | 1.95 | 2175000 | 0.0657 |
0.0598 | 1.97 | 2200000 | 0.0646 |
0.0633 | 1.99 | 2225000 | 0.0645 |
Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.17.0
- Tokenizers 0.10.3
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