bert-base-uncased-finetuned-wnli
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: 0.6913
- Accuracy: 0.5634
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: 128
- eval_batch_size: 128
- 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 | 73 | 0.7008 | 0.4366 |
| No log | 2.0 | 146 | 0.6943 | 0.5211 |
| No log | 3.0 | 219 | 0.6943 | 0.4789 |
| No log | 4.0 | 292 | 0.6913 | 0.5634 |
| No log | 5.0 | 365 | 0.6932 | 0.5634 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cpu
- Datasets 2.10.1
- Tokenizers 0.13.2
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