afro-xlmr-base-vmw-MICRO

This model is a fine-tuned version of Davlan/afro-xlmr-base on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.3214
• F1: 0.3409
• Roc Auc: 0.6282
• Accuracy: 0.5300

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: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 100
• num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	F1	Roc Auc	Accuracy
0.2701	1.0	146	0.2659	0.0	0.5	0.5164
0.2597	2.0	292	0.2704	0.0	0.5	0.5164
0.2792	3.0	438	0.2623	0.0	0.5	0.5164
0.2183	4.0	584	0.2583	0.0534	0.5136	0.5280
0.2156	5.0	730	0.2518	0.1701	0.5466	0.5513
0.1805	6.0	876	0.2535	0.2421	0.5737	0.5416
0.156	7.0	1022	0.2597	0.2715	0.5863	0.5474
0.1261	8.0	1168	0.2728	0.2717	0.5846	0.5571
0.1157	9.0	1314	0.2882	0.3146	0.6135	0.5164
0.0977	10.0	1460	0.2980	0.3073	0.6097	0.5184
0.0762	11.0	1606	0.3144	0.2925	0.6031	0.5126
0.0669	12.0	1752	0.3256	0.3333	0.6275	0.5106
0.0591	13.0	1898	0.3214	0.3409	0.6282	0.5300
0.0461	14.0	2044	0.3286	0.3357	0.6226	0.5358
0.0567	15.0	2190	0.3313	0.3248	0.6190	0.5222
0.0495	16.0	2336	0.3397	0.3102	0.6124	0.5145
0.0363	17.0	2482	0.3370	0.3110	0.6106	0.5300

Framework versions

• Transformers 4.45.1
• Pytorch 2.4.0
• Datasets 3.0.1
• Tokenizers 0.20.0