| | --- |
| | library_name: transformers |
| | license: apache-2.0 |
| | base_model: answerdotai/ModernBERT-base |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - accuracy |
| | - f1 |
| | - precision |
| | - recall |
| | model-index: |
| | - name: results |
| | 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. --> |
| |
|
| | # results |
| |
|
| | This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the None dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.2000 |
| | - Accuracy: 0.9433 |
| | - F1: 0.9429 |
| | - Precision: 0.9508 |
| | - Recall: 0.9433 |
| |
|
| | ## 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: 4e-05 |
| | - train_batch_size: 6 |
| | - eval_batch_size: 6 |
| | - seed: 42 |
| | - gradient_accumulation_steps: 2 |
| | - total_train_batch_size: 12 |
| | - 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: 2 |
| | - mixed_precision_training: Native AMP |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
| | |:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
| | | 3.8028 | 0.0833 | 25 | 1.5191 | 0.3944 | 0.2893 | 0.4598 | 0.3944 | |
| | | 2.2046 | 0.1667 | 50 | 0.7147 | 0.75 | 0.7423 | 0.7685 | 0.75 | |
| | | 1.2172 | 0.25 | 75 | 0.6074 | 0.7989 | 0.7727 | 0.8508 | 0.7989 | |
| | | 0.9054 | 0.3333 | 100 | 0.3817 | 0.8656 | 0.8637 | 0.8907 | 0.8656 | |
| | | 0.873 | 0.4167 | 125 | 0.3460 | 0.8678 | 0.8665 | 0.8810 | 0.8678 | |
| | | 0.7074 | 0.5 | 150 | 0.2918 | 0.8889 | 0.8848 | 0.9159 | 0.8889 | |
| | | 1.0552 | 0.5833 | 175 | 0.2550 | 0.89 | 0.8868 | 0.9130 | 0.89 | |
| | | 0.5167 | 0.6667 | 200 | 0.2660 | 0.9044 | 0.9043 | 0.9071 | 0.9044 | |
| | | 0.3174 | 0.75 | 225 | 0.2641 | 0.8956 | 0.8882 | 0.9235 | 0.8956 | |
| | | 0.3369 | 0.8333 | 250 | 0.1745 | 0.9489 | 0.9490 | 0.9520 | 0.9489 | |
| | | 0.2966 | 0.9167 | 275 | 0.1484 | 0.9567 | 0.9568 | 0.9589 | 0.9567 | |
| | | 0.5544 | 1.0 | 300 | 0.2000 | 0.9433 | 0.9429 | 0.9508 | 0.9433 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.57.1 |
| | - Pytorch 2.8.0+cu126 |
| | - Datasets 4.0.0 |
| | - Tokenizers 0.22.1 |
| |
|
