xlmr_english_immigration

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: 0.2598
• Accuracy: 0.9308
• 1-f1: 0.8966
• 1-recall: 0.9070
• 1-precision: 0.8864
• Balanced Acc: 0.9248

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 with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 30
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	1-f1	1-recall	1-precision	Balanced Acc
0.5549	1.0	5	0.4841	0.8615	0.7353	0.5814	1.0	0.7907
0.5873	2.0	10	0.4579	0.8692	0.8132	0.8605	0.7708	0.8670
0.4552	3.0	15	0.4112	0.9	0.8434	0.8140	0.875	0.8782
0.3566	4.0	20	0.3635	0.8923	0.8205	0.7442	0.9143	0.8549
0.3533	5.0	25	0.3118	0.9231	0.8810	0.8605	0.9024	0.9072
0.3372	6.0	30	0.3078	0.9077	0.8696	0.9302	0.8163	0.9134
0.1963	7.0	35	0.2485	0.9154	0.8706	0.8605	0.8810	0.9015
0.1598	8.0	40	0.2199	0.9154	0.8736	0.8837	0.8636	0.9074
0.1177	9.0	45	0.2188	0.9154	0.8764	0.9070	0.8478	0.9133
0.0412	10.0	50	0.2444	0.9154	0.8764	0.9070	0.8478	0.9133
0.1093	11.0	55	0.2598	0.9308	0.8966	0.9070	0.8864	0.9248

Framework versions

• Transformers 4.56.0.dev0
• Pytorch 2.6.0+cu124
• Datasets 4.0.0
• Tokenizers 0.21.4