| --- |
| 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_0 |
| 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_0 |
|
|
| 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: 0.0362 |
| - Accuracy: 0.9938 |
| - F1: 0.9885 |
| - Precision: 0.9967 |
| - Recall: 0.9805 |
|
|
| ## 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: 20 |
| - eval_batch_size: 20 |
| - 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 | |
| |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:| |
| | 0.063 | 1.0 | 7567 | 0.0503 | 0.9908 | 0.9827 | 0.9980 | 0.9680 | |
| | 0.0004 | 2.0 | 15134 | 0.0351 | 0.9938 | 0.9884 | 0.9942 | 0.9827 | |
| | 0.0122 | 3.0 | 22701 | 0.0362 | 0.9938 | 0.9885 | 0.9967 | 0.9805 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.57.6 |
| - Pytorch 2.11.0+cu128 |
| - Datasets 4.8.4 |
| - Tokenizers 0.22.2 |
| |
