| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: google-bert/bert-base-uncased |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| model-index: |
| - name: bert-webpage-classifier |
| 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-webpage-classifier |
|
|
| This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0136 |
| - Accuracy: 0.996 |
|
|
| ## 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: 5e-05 |
| - train_batch_size: 8 |
| - eval_batch_size: 8 |
| - 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 | |
| |:-------------:|:-----:|:-----:|:---------------:|:--------:| |
| | 0.1497 | 1.0 | 2536 | 0.0185 | 0.996 | |
| | 0.0308 | 2.0 | 5072 | 0.0151 | 0.996 | |
| | 0.0263 | 3.0 | 7608 | 0.0143 | 0.996 | |
| | 0.0245 | 4.0 | 10144 | 0.0140 | 0.996 | |
| | 0.0213 | 5.0 | 12680 | 0.0140 | 0.996 | |
| | 0.0199 | 6.0 | 15216 | 0.0139 | 0.998 | |
| | 0.0184 | 7.0 | 17752 | 0.0133 | 0.996 | |
| | 0.0187 | 8.0 | 20288 | 0.0141 | 0.996 | |
| | 0.0181 | 9.0 | 22824 | 0.0139 | 0.996 | |
| | 0.0174 | 10.0 | 25360 | 0.0136 | 0.996 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.57.1 |
| - Pytorch 2.8.0+cu126 |
| - Datasets 4.0.0 |
| - Tokenizers 0.22.1 |
| |
