mbert-en-finetuned-sinta-e10

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

• Loss: 0.1755
• F1: 0.7669
• Roc Auc: 0.8281
• Accuracy: 0.4681

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: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	F1	Roc Auc	Accuracy
No log	1.0	141	0.2791	0.5873	0.7299	0.1631
No log	2.0	282	0.2282	0.7026	0.7830	0.3475
No log	3.0	423	0.2069	0.7022	0.7853	0.3546
0.2721	4.0	564	0.1903	0.7344	0.8029	0.3901
0.2721	5.0	705	0.1817	0.7467	0.8148	0.4397
0.2721	6.0	846	0.1755	0.7669	0.8281	0.4681
0.2721	7.0	987	0.1706	0.7628	0.8236	0.4539
0.1659	8.0	1128	0.1666	0.7664	0.8292	0.4823
0.1659	9.0	1269	0.1650	0.7626	0.8274	0.4681
0.1659	10.0	1410	0.1645	0.7649	0.8290	0.4681

Framework versions

• Transformers 4.44.2
• Pytorch 2.4.1+cu121
• Datasets 3.0.0
• Tokenizers 0.19.1