| --- |
| library_name: transformers |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - f1 |
| model-index: |
| - name: results |
| 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. --> |
|
|
| # results |
|
|
| This model was trained from scratch on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.1958 |
| - Accuracy: 0.9431 |
| - F1: 0.9434 |
|
|
| ## 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: 42 |
| - gradient_accumulation_steps: 4 |
| - total_train_batch_size: 64 |
| - 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: linear |
| - num_epochs: 3 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
| |:-------------:|:------:|:----:|:---------------:|:--------:|:------:| |
| | 0.2569 | 0.1280 | 50 | 0.2415 | 0.9041 | 0.8996 | |
| | 0.2055 | 0.2559 | 100 | 0.2228 | 0.9157 | 0.9194 | |
| | 0.2424 | 0.3839 | 150 | 0.1831 | 0.9311 | 0.9310 | |
| | 0.2092 | 0.5118 | 200 | 0.1808 | 0.9313 | 0.9316 | |
| | 0.1878 | 0.6398 | 250 | 0.1993 | 0.9244 | 0.9273 | |
| | 0.2077 | 0.7678 | 300 | 0.1705 | 0.9357 | 0.9367 | |
| | 0.1937 | 0.8957 | 350 | 0.1847 | 0.9282 | 0.9310 | |
| | 0.1448 | 1.0230 | 400 | 0.1701 | 0.9366 | 0.9361 | |
| | 0.1034 | 1.1510 | 450 | 0.1763 | 0.9403 | 0.9403 | |
| | 0.1395 | 1.2790 | 500 | 0.1854 | 0.9396 | 0.9401 | |
| | 0.1141 | 1.4069 | 550 | 0.1774 | 0.9389 | 0.9381 | |
| | 0.1323 | 1.5349 | 600 | 0.1716 | 0.9389 | 0.9377 | |
| | 0.1824 | 1.6628 | 650 | 0.1866 | 0.9381 | 0.9398 | |
| | 0.133 | 1.7908 | 700 | 0.1716 | 0.9415 | 0.9414 | |
| | 0.1054 | 1.9187 | 750 | 0.1651 | 0.944 | 0.9442 | |
| | 0.0473 | 2.0461 | 800 | 0.1755 | 0.944 | 0.9440 | |
| | 0.0458 | 2.1740 | 850 | 0.1917 | 0.9426 | 0.9427 | |
| | 0.1082 | 2.3020 | 900 | 0.2014 | 0.9418 | 0.9424 | |
| | 0.0621 | 2.4299 | 950 | 0.2019 | 0.9416 | 0.9418 | |
| | 0.0773 | 2.5579 | 1000 | 0.1988 | 0.9412 | 0.9411 | |
| | 0.1104 | 2.6859 | 1050 | 0.2031 | 0.9418 | 0.9413 | |
| | 0.079 | 2.8138 | 1100 | 0.1962 | 0.9431 | 0.9432 | |
| | 0.0717 | 2.9418 | 1150 | 0.1958 | 0.9431 | 0.9434 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.54.0 |
| - Pytorch 2.6.0+cu124 |
| - Datasets 4.0.0 |
| - Tokenizers 0.21.2 |
| |
