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