modernbert-match-user-16297

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

• Accuracy: 0.5429
• F1: 0.5275
• F1 Class 0: 0.4348
• F1 Class 1: 0.1333
• F1 Class 2: 0.0
• F1 Class 3: 0.2
• F1 Class 4: 0.7719
• Loss: 2.9671
• Precision: 0.5152
• Recall: 0.5429

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: 2e-05
• train_batch_size: 4
• eval_batch_size: 4
• 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: cosine_with_restarts
• lr_scheduler_warmup_steps: 105
• num_epochs: 10

Training results

Training Loss	Epoch	Step	Accuracy	F1	F1 Class 0	F1 Class 1	F1 Class 2	F1 Class 3	F1 Class 4	Validation Loss	Precision	Recall
1.2082	1.0	105	0.5333	0.3889	0.1538	0.0	0.0	0.0	0.6879	2.0737	0.4982	0.5333
1.0302	2.0	210	0.6381	0.5420	0.6667	0.0	0.0	0.0	0.7576	1.3303	0.4736	0.6381
0.7632	3.0	315	0.5905	0.5527	0.6	0.1818	0.0	0.1429	0.752	1.3890	0.5577	0.5905
0.6931	4.0	420	0.6	0.5420	0.6182	0.0	0.0	0.1429	0.75	1.5166	0.5109	0.6
0.702	5.0	525	0.5333	0.5036	0.5	0.1429	0.0	0.0	0.7358	1.5875	0.4898	0.5333
0.5527	6.0	630	0.5429	0.5175	0.4255	0.0	0.0	0.3077	0.7544	2.3720	0.4959	0.5429
0.4185	7.0	735	0.5429	0.5261	0.4255	0.1429	0.0	0.2	0.7719	2.3707	0.5125	0.5429
0.2422	8.0	840	0.5429	0.5262	0.4444	0.1333	0.0	0.2	0.7652	2.9305	0.5140	0.5429
0.1661	9.0	945	0.5429	0.5265	0.4348	0.1333	0.0	0.1905	0.7719	2.9621	0.5129	0.5429
0.2278	10.0	1050	0.5429	0.5275	0.4348	0.1333	0.0	0.2	0.7719	2.9671	0.5152	0.5429

Framework versions

• Transformers 4.49.0
• Pytorch 2.6.0
• Datasets 2.21.0
• Tokenizers 0.21.0