ModernBERT_qnli
This model is a fine-tuned version of answerdotai/ModernBERT-base on GLUE/QNLI dataset. It achieves the following results on the evaluation set:
- Loss: 0.2094
- Accuracy Score: 0.9325
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 8e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.98) and epsilon=1e-06 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy Score |
|---|---|---|---|---|
| 0.2794 | 1.0 | 3274 | 0.1853 | 0.9268 |
| 0.1024 | 2.0 | 6548 | 0.2094 | 0.9325 |
Framework versions
- Transformers 4.49.0.dev0
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
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Model tree for Jsevisal/ModernBERT_qnli
Base model
answerdotai/ModernBERT-base