AraBERT_token_classification_no_merge
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: 1.4408
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
- Accuracy: 0.6786
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: 5
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
No log | 1.0 | 27 | 1.4727 | 0.0 | 0.0 | 0.0 | 0.6786 |
No log | 2.0 | 54 | 1.4639 | 0.0 | 0.0 | 0.0 | 0.6786 |
No log | 3.0 | 81 | 1.4707 | 0.0 | 0.0 | 0.0 | 0.6786 |
No log | 4.0 | 108 | 1.4494 | 0.0 | 0.0 | 0.0 | 0.6786 |
No log | 5.0 | 135 | 1.4408 | 0.0 | 0.0 | 0.0 | 0.6786 |
Framework versions
- Transformers 4.30.2
- Pytorch 1.12.1
- Datasets 2.13.2
- Tokenizers 0.13.3
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