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
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