ModernBERT-domain-classifier

This model is a fine-tuned version of answerdotai/ModernBERT-base on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.5256
• Accuracy: 0.9409
• Precision: 1.0
• Recall: 0.9409
• F1: 0.9696

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: 1e-05
• train_batch_size: 16
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 8
• total_train_batch_size: 128
• optimizer: Use 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: 3
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
5.1956	0.8727	6	0.5418	0.9273	1.0	0.9273	0.9623
5.6924	1.8727	12	0.5299	0.9364	1.0	0.9364	0.9671
5.8209	2.8727	18	0.5256	0.9409	1.0	0.9409	0.9696

Framework versions

• Transformers 4.48.0.dev0
• Pytorch 2.5.0+cu124
• Datasets 3.1.0
• Tokenizers 0.21.0