| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: bert-base-cased |
| tags: |
| - generated_from_trainer |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: Bert-RAdam-XL |
| 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-RAdam-XL |
|
|
| This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.1916 |
| - Precision: 0.8057 |
| - Recall: 0.8640 |
| - F1: 0.8338 |
| - Accuracy: 0.9455 |
|
|
| ## 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: 0.0001 |
| - train_batch_size: 16 |
| - eval_batch_size: 16 |
| - seed: 42 |
| - 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: 5 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | 0.1843 | 1.0 | 1000 | 0.1410 | 0.7841 | 0.8672 | 0.8236 | 0.9436 | |
| | 0.1156 | 2.0 | 2000 | 0.1443 | 0.7981 | 0.8372 | 0.8172 | 0.9438 | |
| | 0.0862 | 3.0 | 3000 | 0.1562 | 0.7947 | 0.8961 | 0.8424 | 0.9477 | |
| | 0.0612 | 4.0 | 4000 | 0.1735 | 0.7976 | 0.8853 | 0.8392 | 0.9470 | |
| | 0.038 | 5.0 | 5000 | 0.1916 | 0.8057 | 0.8640 | 0.8338 | 0.9455 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.51.3 |
| - Pytorch 2.6.0+cu124 |
| - Datasets 3.6.0 |
| - Tokenizers 0.21.1 |
|
|
