my_awesome_model
This model is a fine-tuned version of distilbert/distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.9340
- F1: 0.7508
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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 123
- 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
- num_epochs: 30
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| 0.3199 | 1.0 | 1408 | 0.2834 | 0.6815 |
| 0.2423 | 2.0 | 2816 | 0.2862 | 0.6955 |
| 0.1847 | 3.0 | 4224 | 0.3313 | 0.7126 |
| 0.1411 | 4.0 | 5632 | 0.3398 | 0.7236 |
| 0.1205 | 5.0 | 7040 | 0.4494 | 0.7317 |
| 0.0877 | 6.0 | 8448 | 0.4884 | 0.7301 |
| 0.0780 | 7.0 | 9856 | 0.5614 | 0.7311 |
| 0.0620 | 8.0 | 11264 | 0.5494 | 0.7258 |
| 0.0521 | 9.0 | 12672 | 0.6617 | 0.7215 |
| 0.0423 | 10.0 | 14080 | 0.6533 | 0.7371 |
| 0.0329 | 11.0 | 15488 | 0.7038 | 0.7217 |
| 0.0292 | 12.0 | 16896 | 0.7464 | 0.7322 |
| 0.0301 | 13.0 | 18304 | 0.6408 | 0.7376 |
| 0.0257 | 14.0 | 19712 | 0.7520 | 0.7343 |
| 0.0222 | 15.0 | 21120 | 0.7650 | 0.7379 |
| 0.0134 | 16.0 | 22528 | 0.8251 | 0.7365 |
| 0.0218 | 17.0 | 23936 | 0.7849 | 0.7407 |
| 0.0186 | 18.0 | 25344 | 0.7665 | 0.7385 |
| 0.0086 | 19.0 | 26752 | 0.7849 | 0.7480 |
| 0.0098 | 20.0 | 28160 | 0.8165 | 0.7454 |
| 0.0084 | 21.0 | 29568 | 0.8955 | 0.7432 |
| 0.0082 | 22.0 | 30976 | 0.8867 | 0.7415 |
| 0.0038 | 23.0 | 32384 | 0.8941 | 0.7482 |
| 0.0040 | 24.0 | 33792 | 0.9198 | 0.7461 |
| 0.0030 | 25.0 | 35200 | 0.9340 | 0.7508 |
| 0.0048 | 26.0 | 36608 | 0.9430 | 0.7423 |
| 0.0036 | 27.0 | 38016 | 0.9477 | 0.7450 |
| 0.0042 | 28.0 | 39424 | 0.9695 | 0.7474 |
| 0.0003 | 29.0 | 40832 | 0.9826 | 0.7472 |
| 0.0010 | 30.0 | 42240 | 0.9860 | 0.7482 |
Framework versions
- Transformers 5.10.1
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2