| | --- |
| | tags: |
| | - generated_from_trainer |
| | datasets: |
| | - generator |
| | model-index: |
| | - name: bert-trainer-8b |
| | results: [] |
| | --- |
| | |
| | <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| | should probably proofread and complete it, then remove this comment. --> |
| |
|
| | # bert-trainer-8b |
| |
|
| | This model is a fine-tuned version of [](https://huggingface.co/) on the generator dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 3.1639 |
| |
|
| | ## 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: 32 |
| | - mixed_precision_training: Native AMP |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | |
| | |:-------------:|:-----:|:-----:|:---------------:| |
| | | 6.5416 | 1.0 | 500 | 6.5207 | |
| | | 6.393 | 1.99 | 1000 | 6.3903 | |
| | | 6.2817 | 2.99 | 1500 | 6.3033 | |
| | | 6.2274 | 3.98 | 2000 | 6.2671 | |
| | | 6.179 | 4.98 | 2500 | 6.2431 | |
| | | 6.1684 | 5.98 | 3000 | 6.2309 | |
| | | 6.1244 | 6.97 | 3500 | 6.2114 | |
| | | 6.0879 | 7.97 | 4000 | 6.1932 | |
| | | 6.0643 | 8.96 | 4500 | 6.1791 | |
| | | 6.0481 | 9.96 | 5000 | 6.1638 | |
| | | 6.0231 | 10.96 | 5500 | 6.1581 | |
| | | 5.9987 | 11.95 | 6000 | 6.1365 | |
| | | 5.9989 | 12.95 | 6500 | 6.1194 | |
| | | 5.9535 | 13.94 | 7000 | 6.1095 | |
| | | 5.9139 | 14.94 | 7500 | 6.0890 | |
| | | 5.8462 | 15.94 | 8000 | 6.0224 | |
| | | 5.7689 | 16.93 | 8500 | 5.9266 | |
| | | 5.6137 | 17.93 | 9000 | 5.7195 | |
| | | 4.7163 | 18.92 | 9500 | 4.6131 | |
| | | 4.0877 | 19.92 | 10000 | 4.0903 | |
| | | 3.7832 | 20.92 | 10500 | 3.8340 | |
| | | 3.6104 | 21.91 | 11000 | 3.6572 | |
| | | 3.4615 | 22.91 | 11500 | 3.5278 | |
| | | 3.3661 | 23.9 | 12000 | 3.4201 | |
| | | 3.271 | 24.9 | 12500 | 3.3333 | |
| | | 3.2179 | 25.9 | 13000 | 3.2720 | |
| | | 3.1759 | 26.89 | 13500 | 3.2317 | |
| | | 3.1419 | 27.89 | 14000 | 3.2006 | |
| | | 3.1041 | 28.88 | 14500 | 3.1806 | |
| | | 3.0836 | 29.88 | 15000 | 3.1693 | |
| | | 3.0998 | 30.88 | 15500 | 3.1679 | |
| | | 3.08 | 31.87 | 16000 | 3.1639 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.26.1 |
| | - Pytorch 1.13.1 |
| | - Datasets 2.9.0 |
| | - Tokenizers 0.13.2 |
| | |
