xlm-roberta-base-tat-finetuned

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

• Loss: 0.2873
• F1: 0.5327
• Roc Auc: 0.7359
• Accuracy: 0.595

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: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 100
• num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	F1	Roc Auc	Accuracy
0.5826	1.0	49	0.4235	0.0	0.5	0.16
0.4143	2.0	98	0.4032	0.0	0.5	0.16
0.4081	3.0	147	0.3902	0.0	0.5	0.16
0.3759	4.0	196	0.3631	0.1200	0.5587	0.25
0.3329	5.0	245	0.3312	0.3611	0.6423	0.415
0.3105	6.0	294	0.3192	0.3617	0.6585	0.485
0.2798	7.0	343	0.3094	0.4167	0.6785	0.515
0.2495	8.0	392	0.3122	0.4018	0.6703	0.495
0.221	9.0	441	0.2978	0.4003	0.6657	0.495
0.2025	10.0	490	0.2916	0.4518	0.6885	0.525
0.185	11.0	539	0.2909	0.4958	0.7158	0.58
0.1665	12.0	588	0.2846	0.4907	0.7067	0.555
0.1618	13.0	637	0.2862	0.4783	0.6960	0.55
0.1412	14.0	686	0.2851	0.5188	0.7251	0.57
0.1436	15.0	735	0.2873	0.5327	0.7359	0.595
0.1324	16.0	784	0.2963	0.4886	0.7049	0.545
0.1218	17.0	833	0.2922	0.5150	0.7262	0.57
0.1329	18.0	882	0.2899	0.5068	0.7213	0.575
0.1176	19.0	931	0.2920	0.5189	0.7294	0.575

Framework versions

• Transformers 4.47.0
• Pytorch 2.5.1+cu121
• Datasets 3.2.0
• Tokenizers 0.21.0