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bert-concat
This model is a fine-tuned version of on the generator dataset. It achieves the following results on the evaluation set:
- Loss: 5.9507
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: 0.0005
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 14
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 7.3397 | 0.25 | 500 | 6.6405 |
| 6.5835 | 0.51 | 1000 | 6.5183 |
| 6.4967 | 0.76 | 1500 | 6.4926 |
| 6.451 | 1.01 | 2000 | 6.4507 |
| 6.4104 | 1.26 | 2500 | 6.4097 |
| 6.3868 | 1.52 | 3000 | 6.4019 |
| 6.3717 | 1.77 | 3500 | 6.3789 |
| 6.3361 | 2.02 | 4000 | 6.3596 |
| 6.3099 | 2.28 | 4500 | 6.3345 |
| 6.2807 | 2.53 | 5000 | 6.3050 |
| 6.2578 | 2.78 | 5500 | 6.2843 |
| 6.2356 | 3.03 | 6000 | 6.2735 |
| 6.2017 | 3.29 | 6500 | 6.2527 |
| 6.1837 | 3.54 | 7000 | 6.2277 |
| 6.1682 | 3.79 | 7500 | 6.2102 |
| 6.1443 | 4.04 | 8000 | 6.1917 |
| 6.1128 | 4.3 | 8500 | 6.1767 |
| 6.1034 | 4.55 | 9000 | 6.1678 |
| 6.0838 | 4.8 | 9500 | 6.1552 |
| 6.0641 | 5.06 | 10000 | 6.1401 |
| 6.0417 | 5.31 | 10500 | 6.1350 |
| 6.0247 | 5.56 | 11000 | 6.1123 |
| 6.0125 | 5.81 | 11500 | 6.1082 |
| 6.0028 | 6.07 | 12000 | 6.1022 |
| 5.9788 | 6.32 | 12500 | 6.0895 |
| 5.9739 | 6.57 | 13000 | 6.0828 |
| 5.9545 | 6.83 | 13500 | 6.0687 |
| 5.9441 | 7.08 | 14000 | 6.0652 |
| 5.923 | 7.33 | 14500 | 6.0567 |
| 5.9115 | 7.58 | 15000 | 6.0492 |
| 5.9106 | 7.84 | 15500 | 6.0466 |
| 5.8943 | 8.09 | 16000 | 6.0315 |
| 5.8726 | 8.34 | 16500 | 6.0339 |
| 5.8665 | 8.59 | 17000 | 6.0243 |
| 5.8548 | 8.85 | 17500 | 6.0193 |
| 5.8431 | 9.1 | 18000 | 6.0111 |
| 5.8218 | 9.35 | 18500 | 6.0053 |
| 5.8193 | 9.61 | 19000 | 6.0026 |
| 5.8174 | 9.86 | 19500 | 5.9927 |
| 5.7954 | 10.11 | 20000 | 5.9873 |
| 5.7779 | 10.36 | 20500 | 5.9823 |
| 5.7749 | 10.62 | 21000 | 5.9799 |
| 5.7739 | 10.87 | 21500 | 5.9784 |
| 5.7582 | 11.12 | 22000 | 5.9757 |
| 5.7415 | 11.38 | 22500 | 5.9686 |
| 5.7467 | 11.63 | 23000 | 5.9650 |
| 5.7448 | 11.88 | 23500 | 5.9648 |
| 5.7372 | 12.13 | 24000 | 5.9585 |
| 5.7207 | 12.39 | 24500 | 5.9596 |
| 5.7264 | 12.64 | 25000 | 5.9546 |
| 5.7212 | 12.89 | 25500 | 5.9516 |
| 5.7142 | 13.14 | 26000 | 5.9553 |
| 5.7103 | 13.4 | 26500 | 5.9551 |
| 5.7093 | 13.65 | 27000 | 5.9527 |
| 5.7183 | 13.9 | 27500 | 5.9507 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3
