modernBERT-large-claim-agent_v4
This model is a fine-tuned version of answerdotai/ModernBERT-large on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4724
- Accuracy: 0.8931
- Precision: 0.8293
- Recall: 0.8
- F1: 0.8144
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: 16
- eval_batch_size: 16
- seed: 42
- 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
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.2942 | 1.0 | 151 | 0.3334 | 0.8828 | 0.8493 | 0.7294 | 0.7848 |
| 0.2043 | 2.0 | 302 | 0.2839 | 0.9 | 0.8415 | 0.8118 | 0.8263 |
| 0.1109 | 3.0 | 453 | 0.4724 | 0.8931 | 0.8293 | 0.8 | 0.8144 |
Framework versions
- Transformers 5.12.1
- Pytorch 2.11.0+cu128
- Datasets 5.0.0
- Tokenizers 0.22.2