AraBERT_token_classification__AraEval24

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: 0.8744
• Precision: 0.1001
• Recall: 0.0230
• F1: 0.0374
• Accuracy: 0.8601

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
0.6497	1.0	2851	0.7614	0.0769	0.0007	0.0015	0.8631
0.5817	2.0	5702	0.8128	0.1441	0.0020	0.0039	0.8635
0.5328	3.0	8553	0.7802	0.1538	0.0007	0.0015	0.8634
0.5006	4.0	11404	0.7901	0.1269	0.0021	0.0041	0.8633
0.4445	5.0	14255	0.8134	0.1038	0.0014	0.0027	0.8634
0.4261	6.0	17106	0.8102	0.1135	0.0124	0.0223	0.8623
0.4081	7.0	19957	0.8238	0.1029	0.0131	0.0233	0.8624
0.3831	8.0	22808	0.8346	0.0913	0.0139	0.0241	0.8593
0.3525	9.0	25659	0.8433	0.1044	0.0246	0.0399	0.8601
0.3471	10.0	28510	0.8744	0.1001	0.0230	0.0374	0.8601

Framework versions

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