---
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