| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: distilbert-base-uncased |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| model-index: |
| - name: Learning_file |
| 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. --> |
|
|
| # Learning_file |
| |
| This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.3765 |
| - Accuracy: 0.895 |
| |
| ## 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: 1e-05 |
| - train_batch_size: 32 |
| - eval_batch_size: 32 |
| - 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: 10 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| |
| | 1.2846 | 1.0 | 32 | 1.1305 | 0.745 | |
| | 0.9125 | 2.0 | 64 | 0.7700 | 0.845 | |
| | 0.5789 | 3.0 | 96 | 0.5487 | 0.86 | |
| | 0.4028 | 4.0 | 128 | 0.4588 | 0.87 | |
| | 0.3067 | 5.0 | 160 | 0.4303 | 0.87 | |
| | 0.2694 | 6.0 | 192 | 0.4051 | 0.87 | |
| | 0.2243 | 7.0 | 224 | 0.3891 | 0.89 | |
| | 0.2052 | 8.0 | 256 | 0.3834 | 0.89 | |
| | 0.1907 | 9.0 | 288 | 0.3778 | 0.89 | |
| | 0.1891 | 10.0 | 320 | 0.3765 | 0.895 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.57.2 |
| - Pytorch 2.9.0+cu126 |
| - Datasets 4.0.0 |
| - Tokenizers 0.22.1 |
|
|
