modernBERT-Math-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: 0.8325
• Micro F1: 0.8502

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: 4
• eval_batch_size: 4
• seed: 42
• distributed_type: multi-GPU
• num_devices: 2
• total_train_batch_size: 8
• total_eval_batch_size: 8
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.98) and epsilon=1e-06 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 10
• mixed_precision_training: Native AMP
• label_smoothing_factor: 0.1

Training results

Training Loss	Epoch	Step	Validation Loss	Micro F1
1.3184	1.0	1083	0.9248	0.7986
0.858	2.0	2166	0.8338	0.8424
0.6955	3.0	3249	0.8040	0.8561
0.5752	4.0	4332	0.8284	0.8463
0.5152	5.0	5415	0.8289	0.8502
0.4921	6.0	6498	0.8361	0.8424
0.4807	7.0	7581	0.8297	0.8502
0.4752	8.0	8664	0.8271	0.8561
0.4717	9.0	9747	0.8349	0.8476
0.4696	10.0	10830	0.8325	0.8502

Framework versions

• Transformers 4.51.1
• Pytorch 2.5.1+cu124
• Datasets 3.5.0
• Tokenizers 0.21.0