AraBERT_token_classification__AraEval24_sampl10

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: 0.9733
• Precision: 0.0290
• Recall: 0.0077
• F1: 0.0122
• Accuracy: 0.8674

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
No log	1.0	243	0.8408	0.0	0.0	0.0	0.8767
No log	2.0	486	0.8243	0.0	0.0	0.0	0.8767
0.6988	3.0	729	0.8134	0.0	0.0	0.0	0.8767
0.6988	4.0	972	0.8077	0.0	0.0	0.0	0.8767
0.6379	5.0	1215	0.8552	0.0	0.0	0.0	0.8757
0.6379	6.0	1458	0.8927	0.0161	0.0004	0.0007	0.8763
0.4942	7.0	1701	0.9318	0.0218	0.0026	0.0047	0.8734
0.4942	8.0	1944	0.9273	0.0311	0.0062	0.0103	0.8692
0.3468	9.0	2187	0.9655	0.0273	0.0063	0.0103	0.8685
0.3468	10.0	2430	0.9733	0.0290	0.0077	0.0122	0.8674

Framework versions

• Transformers 4.30.2
• Pytorch 1.12.1
• Datasets 2.13.2
• Tokenizers 0.13.3