AraBERT_token_classification__AraEval24_truncated
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: 1.7905
- Precision: 0.2295
- Recall: 0.1820
- F1: 0.2031
- Accuracy: 0.6274
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 |
---|---|---|---|---|---|---|---|
1.563 | 1.0 | 798 | 1.5140 | 0.1409 | 0.0447 | 0.0679 | 0.6364 |
1.1924 | 2.0 | 1596 | 1.4530 | 0.1759 | 0.0791 | 0.1091 | 0.6424 |
1.0371 | 3.0 | 2394 | 1.4860 | 0.2043 | 0.1060 | 0.1396 | 0.6392 |
0.8527 | 4.0 | 3192 | 1.5512 | 0.2062 | 0.1285 | 0.1584 | 0.6292 |
0.772 | 5.0 | 3990 | 1.5781 | 0.2123 | 0.1545 | 0.1789 | 0.6175 |
0.6296 | 6.0 | 4788 | 1.6486 | 0.2398 | 0.1559 | 0.1890 | 0.6288 |
0.5614 | 7.0 | 5586 | 1.6764 | 0.2226 | 0.1797 | 0.1989 | 0.6246 |
0.5119 | 8.0 | 6384 | 1.7380 | 0.2247 | 0.1875 | 0.2044 | 0.6210 |
0.4581 | 9.0 | 7182 | 1.7726 | 0.2389 | 0.1691 | 0.1980 | 0.6347 |
0.4594 | 10.0 | 7980 | 1.7905 | 0.2295 | 0.1820 | 0.2031 | 0.6274 |
Framework versions
- Transformers 4.30.2
- Pytorch 1.12.1
- Datasets 2.13.2
- Tokenizers 0.13.3
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