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bert-concat-2
This model is a fine-tuned version of on the generator dataset. It achieves the following results on the evaluation set:
- Loss: 5.7060
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: 20
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 6.6866 | 0.52 | 1000 | 6.2709 |
| 6.2315 | 1.04 | 2000 | 6.2177 |
| 6.1818 | 1.56 | 3000 | 6.1895 |
| 6.1511 | 2.08 | 4000 | 6.1559 |
| 6.0984 | 2.6 | 5000 | 6.1185 |
| 6.0611 | 3.12 | 6000 | 6.0668 |
| 6.0114 | 3.65 | 7000 | 6.0361 |
| 5.9679 | 4.17 | 8000 | 6.0160 |
| 5.9272 | 4.69 | 9000 | 5.9731 |
| 5.8904 | 5.21 | 10000 | 5.9424 |
| 5.8557 | 5.73 | 11000 | 5.9190 |
| 5.8237 | 6.25 | 12000 | 5.9002 |
| 5.8008 | 6.77 | 13000 | 5.8787 |
| 5.7785 | 7.29 | 14000 | 5.8644 |
| 5.7569 | 7.81 | 15000 | 5.8534 |
| 5.7305 | 8.33 | 16000 | 5.8429 |
| 5.7187 | 8.85 | 17000 | 5.8283 |
| 5.699 | 9.38 | 18000 | 5.8124 |
| 5.6737 | 9.9 | 19000 | 5.8055 |
| 5.648 | 10.42 | 20000 | 5.7945 |
| 5.641 | 10.94 | 21000 | 5.7869 |
| 5.613 | 11.46 | 22000 | 5.7700 |
| 5.6078 | 11.98 | 23000 | 5.7659 |
| 5.5759 | 12.5 | 24000 | 5.7555 |
| 5.5682 | 13.02 | 25000 | 5.7522 |
| 5.5461 | 13.54 | 26000 | 5.7397 |
| 5.5414 | 14.06 | 27000 | 5.7349 |
| 5.5195 | 14.58 | 28000 | 5.7310 |
| 5.5081 | 15.1 | 29000 | 5.7214 |
| 5.4922 | 15.62 | 30000 | 5.7188 |
| 5.4858 | 16.15 | 31000 | 5.7127 |
| 5.4786 | 16.67 | 32000 | 5.7092 |
| 5.4685 | 17.19 | 33000 | 5.7075 |
| 5.4571 | 17.71 | 34000 | 5.7060 |
| 5.4592 | 18.23 | 35000 | 5.7018 |
| 5.4555 | 18.75 | 36000 | 5.7043 |
| 5.4512 | 19.27 | 37000 | 5.7028 |
| 5.4522 | 19.79 | 38000 | 5.7060 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3
