output_fp16

This model is a fine-tuned version of xlm-roberta-base on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.4230
• Accuracy: 0.8382

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: 0.0003
• train_batch_size: 32
• eval_batch_size: 8
• 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: 15
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.5485	1.0	12272	0.4865	0.8100
0.4989	2.0	24544	0.4720	0.8193
0.4743	3.0	36816	0.5417	0.7859
0.4762	4.0	49088	0.4359	0.8313
0.4525	5.0	61360	0.4297	0.8365
0.4457	6.0	73632	0.4273	0.8398
0.4205	7.0	85904	0.4343	0.8321
0.4315	8.0	98176	0.4287	0.8357
0.4271	9.0	110448	0.4299	0.8394
0.4031	10.0	122720	0.4250	0.8353
0.4	11.0	134992	0.4401	0.8345
0.3899	12.0	147264	0.4178	0.8418
0.3921	13.0	159536	0.4313	0.8386
0.3849	14.0	171808	0.4212	0.8378
0.3777	15.0	184080	0.4230	0.8382

Framework versions

• Transformers 4.53.2
• Pytorch 2.6.0+cu124
• Datasets 2.18.0
• Tokenizers 0.21.2