xlmr_all_immigration4

This model is a fine-tuned version of FacebookAI/xlm-roberta-large on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.3429
• Accuracy: 0.9
• 1-f1: 0.8354
• 1-recall: 0.7674
• 1-precision: 0.9167
• Balanced Acc: 0.8665

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: 128
• eval_batch_size: 128
• seed: 42
• optimizer: Use OptimizerNames.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: 15
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	1-f1	1-recall	1-precision	Balanced Acc
0.601	1.0	5	0.6534	0.6692	0.0	0.0	0.0	0.5
0.6427	2.0	10	0.6305	0.6692	0.0	0.0	0.0	0.5
0.6068	3.0	15	0.6400	0.6692	0.0	0.0	0.0	0.5
0.7139	4.0	20	0.6143	0.6692	0.0	0.0	0.0	0.5
0.5395	5.0	25	0.5969	0.6692	0.0	0.0	0.0	0.5
0.5974	6.0	30	0.5701	0.7	0.1702	0.0930	1.0	0.5465
0.5214	7.0	35	0.5203	0.8077	0.5902	0.4186	1.0	0.7093
0.4061	8.0	40	0.4794	0.8615	0.7429	0.6047	0.9630	0.7966
0.4453	9.0	45	0.4435	0.8692	0.7671	0.6512	0.9333	0.8141
0.3981	10.0	50	0.4033	0.8692	0.7848	0.7209	0.8611	0.8317
0.4108	11.0	55	0.3717	0.8923	0.8205	0.7442	0.9143	0.8549
0.226	12.0	60	0.3681	0.8769	0.8049	0.7674	0.8462	0.8492
0.3163	13.0	65	0.3546	0.8846	0.8148	0.7674	0.8684	0.8550
0.2189	14.0	70	0.3438	0.8923	0.8205	0.7442	0.9143	0.8549
0.2799	15.0	75	0.3429	0.9	0.8354	0.7674	0.9167	0.8665

Framework versions

• Transformers 4.56.0.dev0
• Pytorch 2.8.0+cu126
• Datasets 4.0.0
• Tokenizers 0.21.4