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distilbert-base-uncased-finetuned-FYP
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0921
- Accuracy: 0.9957
- F1: 0.9957
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: 64
- eval_batch_size: 64
- 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 | Accuracy | F1 |
---|---|---|---|---|---|
2.1435 | 1.0 | 20 | 1.7903 | 0.7696 | 0.7462 |
1.5449 | 2.0 | 40 | 1.0549 | 0.9565 | 0.9603 |
1.0008 | 3.0 | 60 | 0.5800 | 0.9913 | 0.9912 |
0.6252 | 4.0 | 80 | 0.3311 | 0.9957 | 0.9957 |
0.3833 | 5.0 | 100 | 0.2076 | 0.9957 | 0.9957 |
0.2496 | 6.0 | 120 | 0.1470 | 0.9957 | 0.9957 |
0.182 | 7.0 | 140 | 0.1173 | 0.9957 | 0.9957 |
0.1475 | 8.0 | 160 | 0.1017 | 0.9957 | 0.9957 |
0.1279 | 9.0 | 180 | 0.0944 | 0.9957 | 0.9957 |
0.1197 | 10.0 | 200 | 0.0921 | 0.9957 | 0.9957 |
Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3