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USS-reward-model-utterance-WRS
athirorg/USS-reward-model-utterance-WRS
USS-reward-model-utterance-WRS is a machine learning model from athirorg. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- transformers
- License
- apache-2.0
- Model type
- modernbert
- Access
- Public
- Created
- Jun 10, 2026
- Updated
- Jun 10, 2026
- SHA
- fc0012d3
Base models
Model card
From the Hugging Face model README
<!-- 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-utterance-WRS
This model is a fine-tuned version of answerdotai/ModernBERT-large on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5460
- Mse: 0.2326
- Mae: 0.3471
- R2: -1.1275
- Spearman Correlation: -0.0277
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: 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 | Mse | Mae | R2 | Spearman Correlation |
|---|---|---|---|---|---|---|---|
| 46.7627 | 1.0 | 591 | 0.5256 | 0.4280 | 0.5110 | -2.9143 | -0.0244 |
| 19.9530 | 2.0 | 1182 | 0.5341 | 0.4089 | 0.5007 | -2.7388 | 0.0013 |
| 5.2161 | 3.0 | 1773 | 0.5949 | 0.3394 | 0.4523 | -2.1036 | -0.0037 |
| 2.1788 | 4.0 | 2364 | 0.7364 | 0.3554 | 0.4675 | -2.2503 | -0.0119 |
| 1.3887 | 5.0 | 2955 | 0.5760 | 0.3011 | 0.4235 | -1.7535 | -0.0242 |
| 1.0354 | 6.0 | 3546 | 0.6582 | 0.2985 | 0.4157 | -1.7300 | -0.0264 |
| 0.5253 | 7.0 | 4137 | 0.4425 | 0.2575 | 0.3813 | -1.3543 | -0.0119 |
| 0.3577 | 8.0 | 4728 | 0.6235 | 0.2456 | 0.3625 | -1.2457 | -0.0354 |
| 0.2553 | 9.0 | 5319 | 0.5635 | 0.2417 | 0.3541 | -1.2103 | -0.0295 |
| 0.1649 | 10.0 | 5910 | 0.5460 | 0.2326 | 0.3471 | -1.1275 | -0.0277 |
Framework versions
- Transformers 5.9.0
- Pytorch 2.12.0+cu130
- Datasets 4.8.5
- Tokenizers 0.22.2
- Library
- transformers
- Type
- modernbert
- License
- apache-2.0
- Created
- Jun 10, 2026
- Updated
- Jun 10, 2026