| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: distilbert/distilbert-base-uncased |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - f1 |
| model-index: |
| - name: router |
| 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. --> |
|
|
| # router |
|
|
| This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.2080 |
| - Accuracy: 0.9221 |
| - F1: 0.7601 |
|
|
| ## 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: 5e-06 |
| - train_batch_size: 128 |
| - eval_batch_size: 128 |
| - seed: 42 |
| - 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: 20 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
| |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:| |
| | 0.2283 | 1.0 | 990 | 0.1792 | 0.9208 | 0.7547 | |
| | 0.1719 | 2.0 | 1980 | 0.1717 | 0.9256 | 0.7648 | |
| | 0.1621 | 3.0 | 2970 | 0.1695 | 0.9285 | 0.7747 | |
| | 0.1555 | 4.0 | 3960 | 0.1708 | 0.9261 | 0.7695 | |
| | 0.1496 | 5.0 | 4950 | 0.1696 | 0.9271 | 0.7673 | |
| | 0.1455 | 6.0 | 5940 | 0.1716 | 0.9268 | 0.7648 | |
| | 0.1400 | 7.0 | 6930 | 0.1727 | 0.9257 | 0.7681 | |
| | 0.1351 | 8.0 | 7920 | 0.1756 | 0.9268 | 0.7700 | |
| | 0.1309 | 9.0 | 8910 | 0.1812 | 0.9249 | 0.7667 | |
| | 0.1273 | 10.0 | 9900 | 0.1817 | 0.9244 | 0.7654 | |
| | 0.1225 | 11.0 | 10890 | 0.1868 | 0.9216 | 0.7629 | |
| | 0.1192 | 12.0 | 11880 | 0.1903 | 0.9258 | 0.7687 | |
| | 0.1160 | 13.0 | 12870 | 0.1921 | 0.9225 | 0.7638 | |
| | 0.1134 | 14.0 | 13860 | 0.1955 | 0.9228 | 0.7604 | |
| | 0.1115 | 15.0 | 14850 | 0.2012 | 0.9238 | 0.7625 | |
| | 0.1085 | 16.0 | 15840 | 0.2024 | 0.9238 | 0.7598 | |
| | 0.1073 | 17.0 | 16830 | 0.2047 | 0.9228 | 0.7618 | |
| | 0.1049 | 18.0 | 17820 | 0.2059 | 0.9225 | 0.7613 | |
| | 0.1049 | 19.0 | 18810 | 0.2063 | 0.9229 | 0.7641 | |
| | 0.1041 | 20.0 | 19800 | 0.2080 | 0.9221 | 0.7601 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 5.12.1 |
| - Pytorch 2.11.0+cu128 |
| - Datasets 4.0.0 |
| - Tokenizers 0.22.2 |
| |
