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USS-reward-model-utterance-WRS-with-history
athirorg/USS-reward-model-utterance-WRS-with-history
USS-reward-model-utterance-WRS-with-history 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 18, 2026
- Updated
- Jun 18, 2026
- SHA
- 07cb50b1
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-with-history
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: 0.3300
- Mse: 0.3823
- Mae: 0.4761
- R2: -2.4958
- Spearman Correlation: 0.0640
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 |
|---|---|---|---|---|---|---|---|
| 381.4903 | 1.0 | 591 | 0.6530 | 1.7919 | 1.1443 | -15.3861 | 0.0310 |
| 16.2013 | 2.0 | 1182 | 0.5742 | 0.5963 | 0.6639 | -4.4532 | 0.0850 |
| 5.5336 | 3.0 | 1773 | 0.2895 | 0.4737 | 0.5365 | -3.3320 | 0.0462 |
| 2.8279 | 4.0 | 2364 | 0.3505 | 0.4122 | 0.5287 | -2.7691 | 0.0790 |
| 3.2694 | 5.0 | 2955 | 0.3300 | 0.3823 | 0.4761 | -2.4958 | 0.0640 |
| 1.6599 | 6.0 | 3546 | 0.4102 | 0.3957 | 0.5109 | -2.6188 | 0.0463 |
| 0.9408 | 7.0 | 4137 | 0.3741 | 0.4329 | 0.5429 | -2.9583 | 0.0696 |
| 0.5684 | 8.0 | 4728 | 0.4297 | 0.4305 | 0.5453 | -2.9370 | 0.0622 |
| 0.3659 | 9.0 | 5319 | 0.4726 | 0.3883 | 0.5074 | -2.5505 | 0.0467 |
| 0.2385 | 10.0 | 5910 | 0.4093 | 0.4056 | 0.5276 | -2.7089 | 0.0604 |
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 18, 2026
- Updated
- Jun 18, 2026