| | --- |
| | library_name: transformers |
| | license: apache-2.0 |
| | base_model: google-bert/bert-base-uncased |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - accuracy |
| | model-index: |
| | - name: BERT-Router-v1 |
| | 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-Router-v1 |
| |
|
| | This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.1199 |
| | - Accuracy: 0.955 |
| | - Auc: 0.992 |
| |
|
| | ## 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: 256 |
| | - eval_batch_size: 256 |
| | - seed: 42 |
| | - gradient_accumulation_steps: 2 |
| | - total_train_batch_size: 512 |
| | - optimizer: Use 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: 10 |
| | - mixed_precision_training: Native AMP |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | Auc | |
| | |:-------------:|:------:|:----:|:---------------:|:--------:|:-----:| |
| | | 0.134 | 2.1778 | 50 | 0.1322 | 0.949 | 0.991 | |
| | | 0.1212 | 4.3556 | 100 | 0.1267 | 0.951 | 0.991 | |
| | | 0.1199 | 6.5333 | 150 | 0.1223 | 0.953 | 0.992 | |
| | | 0.1193 | 8.7111 | 200 | 0.1199 | 0.955 | 0.992 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.48.3 |
| | - Pytorch 2.5.1+cu121 |
| | - Datasets 3.3.0 |
| | - Tokenizers 0.21.0 |
| | |
