AraBERT_token_classification_AraEval24_multi_span_n_duplicates

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

• Loss: 1.9263
• Precision: 0.1033
• Recall: 0.1042
• F1: 0.1037
• Accuracy: 0.6847

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: 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	Precision	Recall	F1	Accuracy
1.2794	1.0	777	1.3310	0.0297	0.0020	0.0037	0.7241
1.0082	2.0	1554	1.2594	0.1001	0.0446	0.0617	0.7157
0.8023	3.0	2331	1.2844	0.0915	0.0608	0.0731	0.7048
0.5636	4.0	3108	1.4725	0.1060	0.1015	0.1037	0.6721
0.4528	5.0	3885	1.5490	0.1042	0.1042	0.1042	0.6820
0.317	6.0	4662	1.6573	0.1091	0.1075	0.1083	0.6858
0.2821	7.0	5439	1.7894	0.1089	0.1128	0.1108	0.6812
0.2045	8.0	6216	1.8657	0.1059	0.0995	0.1026	0.6892
0.1931	9.0	6993	1.8792	0.1015	0.1128	0.1068	0.6798
0.1587	10.0	7770	1.9263	0.1033	0.1042	0.1037	0.6847

Framework versions

• Transformers 4.30.2
• Pytorch 2.3.1+cu121
• Datasets 2.20.0
• Tokenizers 0.13.3