xlm-roberta-base-ukr-noaug

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: 0.2178
• F1: 0.5613
• Roc Auc: 0.7600
• Accuracy: 0.6747

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
0.4176	1.0	78	0.2861	0.0	0.5	0.4940
0.2848	2.0	156	0.2879	0.0	0.5	0.4940
0.2775	3.0	234	0.2833	0.0	0.5	0.4940
0.2709	4.0	312	0.2631	0.0392	0.5106	0.5060
0.2349	5.0	390	0.2428	0.1379	0.5491	0.5582
0.1911	6.0	468	0.2338	0.2897	0.6182	0.5783
0.1711	7.0	546	0.2026	0.3843	0.6657	0.6305
0.1439	8.0	624	0.1988	0.3878	0.6753	0.6305
0.1212	9.0	702	0.2118	0.4548	0.7086	0.6506
0.1051	10.0	780	0.2096	0.4055	0.6682	0.6426
0.0989	11.0	858	0.2140	0.5377	0.7450	0.6426
0.0859	12.0	936	0.2148	0.5165	0.7336	0.6707
0.0716	13.0	1014	0.2178	0.5613	0.7600	0.6747
0.0645	14.0	1092	0.2155	0.5161	0.7311	0.6787
0.0679	15.0	1170	0.2178	0.5612	0.7494	0.6867
0.0554	16.0	1248	0.2145	0.5424	0.7449	0.6948
0.0609	17.0	1326	0.2121	0.5536	0.7499	0.6867

Framework versions

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