OBert
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5998
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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 251
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 6.5379 | 10.0452 | 20000 | 0.9408 |
| 3.4360 | 20.0904 | 40000 | 0.8064 |
| 2.9475 | 30.1357 | 60000 | 0.7386 |
| 2.6666 | 40.1809 | 80000 | 0.7093 |
| 2.4730 | 50.2261 | 100000 | 0.6868 |
| 2.3229 | 60.2713 | 120000 | 0.6716 |
| 2.2039 | 70.3165 | 140000 | 0.6586 |
| 2.1075 | 80.3618 | 160000 | 0.6550 |
| 2.0190 | 90.4070 | 180000 | 0.6472 |
| 1.9435 | 100.4522 | 200000 | 0.6215 |
| 1.8781 | 110.4974 | 220000 | 0.6290 |
| 1.8177 | 120.5426 | 240000 | 0.6302 |
| 1.7646 | 130.5879 | 260000 | 0.6168 |
| 1.7101 | 140.6331 | 280000 | 0.6162 |
| 1.6629 | 150.6783 | 300000 | 0.5982 |
| 1.6214 | 160.7235 | 320000 | 0.5996 |
| 1.5817 | 170.7687 | 340000 | 0.6098 |
| 1.5453 | 180.8140 | 360000 | 0.5998 |
Framework versions
- Transformers 5.6.2
- Pytorch 2.11.0+cu130
- Datasets 4.8.4
- Tokenizers 0.22.2