results

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

• Loss: 0.6689
• Accuracy: 0.7902
• Precision: 0.7313
• Recall: 0.7061
• F1: 0.7185
• Roc Auc: 0.8589

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: 5e-06
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH 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: 500
• num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1	Roc Auc
0.7761	0.3236	500	0.6084	0.6526	0.5325	0.6840	0.5988	0.7296
0.5502	0.6472	1000	0.5451	0.7265	0.7101	0.4707	0.5661	0.7795
0.5195	0.9709	1500	0.5297	0.7385	0.7838	0.4284	0.5540	0.8112
0.4130	1.2945	2000	0.5337	0.7560	0.7949	0.4803	0.5988	0.8369
0.3740	1.6181	2500	0.4995	0.7713	0.7277	0.6340	0.6776	0.8407
0.3631	1.9417	3000	0.4741	0.7793	0.7254	0.6724	0.6979	0.8511
0.2228	2.2654	3500	0.6620	0.7804	0.7325	0.6628	0.6959	0.8547
0.1807	2.5890	4000	0.7263	0.7881	0.7729	0.6244	0.6908	0.8538
0.1802	2.9126	4500	0.6727	0.7910	0.7371	0.6974	0.7167	0.8587
0.1802	3.0	4635	0.6689	0.7902	0.7313	0.7061	0.7185	0.8589

Framework versions

• Transformers 5.18.0
• Pytorch 2.11.0+cu130
• Datasets 4.8.5
• Tokenizers 0.23.2