| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: bert-large-uncased |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - f1 |
| - precision |
| - recall |
| model-index: |
| - name: bert-large-uncased_fold_3 |
| 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. --> |
|
|
| # bert-large-uncased_fold_3 |
|
|
| This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0310 |
| - Accuracy: 0.9941 |
| - F1: 0.9889 |
| - Precision: 0.9973 |
| - Recall: 0.9806 |
|
|
| ## 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: 15 |
| - eval_batch_size: 15 |
| - 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: 3 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
| |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:| |
| | 0.0499 | 1.0 | 10089 | 0.0392 | 0.9932 | 0.9872 | 0.9951 | 0.9795 | |
| | 0.0303 | 2.0 | 20178 | 0.0364 | 0.9936 | 0.9880 | 0.9929 | 0.9833 | |
| | 0.0228 | 3.0 | 30267 | 0.0310 | 0.9941 | 0.9889 | 0.9973 | 0.9806 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.57.6 |
| - Pytorch 2.11.0+cu128 |
| - Datasets 4.8.4 |
| - Tokenizers 0.22.2 |
| |
