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: 0.0001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- total_eval_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.25
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.598 | 0.1182 | 30 | 1.4674 |
| 0.9568 | 0.2365 | 60 | 0.9714 |
| 0.878 | 0.3547 | 90 | 0.8730 |
| 0.8978 | 0.4729 | 120 | 0.8298 |
| 0.7648 | 0.5911 | 150 | 0.7882 |
| 0.7389 | 0.7094 | 180 | 0.7603 |
| 0.7876 | 0.8276 | 210 | 0.7392 |
| 0.7791 | 0.9458 | 240 | 0.7206 |
| 0.6523 | 1.0631 | 270 | 0.7225 |
| 0.6282 | 1.1813 | 300 | 0.7122 |
| 0.5979 | 1.2995 | 330 | 0.7028 |
| 0.594 | 1.4177 | 360 | 0.6956 |
| 0.6003 | 1.5360 | 390 | 0.6844 |
| 0.5274 | 1.6542 | 420 | 0.6777 |
| 0.5692 | 1.7724 | 450 | 0.6741 |
| 0.5754 | 1.8906 | 480 | 0.6712 |
Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu118
- Datasets 3.5.0
- Tokenizers 0.21.1
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