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recentmodel
This model is a fine-tuned version of aubmindlab/bert-base-arabertv01 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0002
- Accuracy: 1.0
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: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy |
---|---|---|---|---|
0.1073 | 0.09 | 50 | 0.0650 | 0.9867 |
0.0647 | 0.17 | 100 | 0.0491 | 0.9867 |
0.0075 | 0.26 | 150 | 0.0397 | 0.9933 |
0.0441 | 0.34 | 200 | 0.0314 | 0.9933 |
0.0533 | 0.43 | 250 | 0.0082 | 0.9933 |
0.017 | 0.52 | 300 | 0.0006 | 1.0 |
0.0371 | 0.6 | 350 | 0.0310 | 0.9933 |
0.0385 | 0.69 | 400 | 0.0722 | 0.9733 |
0.0149 | 0.78 | 450 | 0.0012 | 1.0 |
0.0236 | 0.86 | 500 | 0.0059 | 0.9933 |
0.0056 | 0.95 | 550 | 0.0187 | 0.9933 |
0.0241 | 1.03 | 600 | 0.0140 | 0.9933 |
0.0003 | 1.12 | 650 | 0.0005 | 1.0 |
0.0115 | 1.21 | 700 | 0.0010 | 1.0 |
0.0042 | 1.29 | 750 | 0.0034 | 1.0 |
0.0003 | 1.38 | 800 | 0.0403 | 0.9933 |
0.0002 | 1.47 | 850 | 0.0005 | 1.0 |
0.0001 | 1.55 | 900 | 0.0095 | 0.9933 |
0.0019 | 1.64 | 950 | 0.0002 | 1.0 |
0.0001 | 1.72 | 1000 | 0.0088 | 0.9933 |
0.0075 | 1.81 | 1050 | 0.0994 | 0.9867 |
0.0002 | 1.9 | 1100 | 0.0523 | 0.9867 |
0.0001 | 1.98 | 1150 | 0.0141 | 0.9933 |
0.0007 | 2.07 | 1200 | 0.0591 | 0.9867 |
0.0001 | 2.16 | 1250 | 0.1006 | 0.9867 |
0.0107 | 2.24 | 1300 | 0.0406 | 0.9933 |
0.0001 | 2.33 | 1350 | 0.0387 | 0.9933 |
0.0001 | 2.41 | 1400 | 0.0377 | 0.9933 |
0.0002 | 2.5 | 1450 | 0.0003 | 1.0 |
0.0 | 2.59 | 1500 | 0.0003 | 1.0 |
0.0 | 2.67 | 1550 | 0.0003 | 1.0 |
0.0 | 2.76 | 1600 | 0.0002 | 1.0 |
0.0 | 2.84 | 1650 | 0.0002 | 1.0 |
0.0 | 2.93 | 1700 | 0.0002 | 1.0 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3