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bert-base-uncased-finetuned-clinc_oos
This model is a fine-tuned version of bert-base-uncased on the clinc_oos dataset. It achieves the following results on the evaluation set:
- Loss: 0.7744
- Accuracy: {'accuracy': 0.932258064516129}
- F1: {'f1': 0.9301680056033511}
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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 4.2947 | 1.0 | 954 | 2.1707 | {'accuracy': 0.8312903225806452} | {'f1': 0.8144079203282508} |
| 1.7379 | 2.0 | 1908 | 1.0298 | {'accuracy': 0.9209677419354839} | {'f1': 0.9177062984730477} |
| 0.8752 | 3.0 | 2862 | 0.7744 | {'accuracy': 0.932258064516129} | {'f1': 0.9301680056033511} |
Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0
