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distilbert-base-uncased-Mixed-swap
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.6885
- Accuracy: 0.7443
- F1: 0.7416
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: 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: 10
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
---|---|---|---|---|---|
0.6532 | 1.0 | 316 | 0.6978 | 0.7005 | 0.6579 |
0.4486 | 2.0 | 632 | 0.8290 | 0.7247 | 0.6718 |
0.3013 | 3.0 | 948 | 0.8289 | 0.7322 | 0.7222 |
0.2025 | 4.0 | 1264 | 1.0831 | 0.7398 | 0.7117 |
0.1332 | 5.0 | 1580 | 1.2624 | 0.7322 | 0.7261 |
0.0993 | 6.0 | 1896 | 1.3924 | 0.7352 | 0.7360 |
0.0646 | 7.0 | 2212 | 1.5108 | 0.7337 | 0.7280 |
0.0508 | 8.0 | 2528 | 1.6213 | 0.7458 | 0.7359 |
0.0369 | 9.0 | 2844 | 1.6694 | 0.7337 | 0.7338 |
0.0327 | 10.0 | 3160 | 1.6885 | 0.7443 | 0.7416 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3