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bert-base-uncased-finetuned-qnli
This model is a fine-tuned version of bert-base-uncased on the glue dataset. It achieves the following results on the evaluation set:
- Loss: 0.6268
- Accuracy: 0.7917
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 63 | 0.5339 | 0.7620 |
| No log | 2.0 | 126 | 0.4728 | 0.7866 |
| No log | 3.0 | 189 | 0.5386 | 0.7847 |
| No log | 4.0 | 252 | 0.6096 | 0.7904 |
| No log | 5.0 | 315 | 0.6268 | 0.7917 |
Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.1
- Tokenizers 0.10.3
