bert-base-uncased-finetuned-swag
This model is a fine-tuned version of bert-base-uncased on the swag dataset. It achieves the following results on the evaluation set:
- Loss: 1.0341
- Accuracy: 0.7912
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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- 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 |
|---|---|---|---|---|
| 0.7441 | 1.0 | 4597 | 0.6021 | 0.7666 |
| 0.375 | 2.0 | 9194 | 0.6227 | 0.7862 |
| 0.1344 | 3.0 | 13791 | 1.0341 | 0.7912 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.10.0
- Tokenizers 0.13.2
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