| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: answerdotai/ModernBERT-large |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - f1 |
| model-index: |
| - name: USS-reward-model-binary-2vs4 |
| 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. --> |
|
|
| # USS-reward-model-binary-2vs4 |
|
|
| This model is a fine-tuned version of [answerdotai/ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.3419 |
| - Accuracy: 0.9104 |
| - F1: 0.9516 |
| - Auc Roc: 0.9121 |
| - Mcc: 0.4764 |
| - Confusion Matrix: [[2, 6], [0, 59]] |
|
|
| ## 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: 2 |
| - eval_batch_size: 1 |
| - seed: 42 |
| - distributed_type: multi-GPU |
| - gradient_accumulation_steps: 10 |
| - total_train_batch_size: 20 |
| - 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 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Auc Roc | Mcc | Confusion Matrix | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:-------:|:------:|:------------------:| |
| | 4.9132 | 1.0 | 24 | 0.4302 | 0.8806 | 0.9365 | 0.7479 | 0.0 | [[0, 8], [0, 59]] | |
| | 4.2935 | 2.0 | 48 | 0.4398 | 0.8806 | 0.9365 | 0.7839 | 0.0 | [[0, 8], [0, 59]] | |
| | 3.8073 | 3.0 | 72 | 0.2622 | 0.8955 | 0.9421 | 0.8729 | 0.4209 | [[3, 5], [2, 57]] | |
| | 2.1451 | 4.0 | 96 | 0.3419 | 0.9104 | 0.9516 | 0.9121 | 0.4764 | [[2, 6], [0, 59]] | |
| | 0.9600 | 5.0 | 120 | 0.9652 | 0.8209 | 0.8889 | 0.875 | 0.5037 | [[7, 1], [11, 48]] | |
| | 0.5732 | 6.0 | 144 | 0.8316 | 0.8657 | 0.9244 | 0.8443 | 0.3257 | [[3, 5], [4, 55]] | |
| | 0.1811 | 7.0 | 168 | 0.9996 | 0.9104 | 0.9516 | 0.8559 | 0.4764 | [[2, 6], [0, 59]] | |
| | 0.2513 | 8.0 | 192 | 0.7501 | 0.8955 | 0.9421 | 0.8729 | 0.4209 | [[3, 5], [2, 57]] | |
| | 0.0001 | 9.0 | 216 | 0.8585 | 0.8955 | 0.9421 | 0.8792 | 0.4209 | [[3, 5], [2, 57]] | |
| | 0.0000 | 10.0 | 240 | 0.8530 | 0.8806 | 0.9333 | 0.8771 | 0.3681 | [[3, 5], [3, 56]] | |
| |
| |
| ### Framework versions |
| |
| - Transformers 5.9.0 |
| - Pytorch 2.12.0+cu130 |
| - Datasets 4.8.5 |
| - Tokenizers 0.22.2 |
| |
