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distilbert-swag
This model is a fine-tuned version of distilbert-base-uncased on the swag dataset. It achieves the following results on the evaluation set:
- Loss: 0.9079
- Accuracy: 0.7105
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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- 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 |
|---|---|---|---|---|
| 0.9186 | 1.0 | 2298 | 0.7658 | 0.6950 |
| 0.5843 | 2.0 | 4597 | 0.7453 | 0.7059 |
| 0.3548 | 3.0 | 6894 | 0.9079 | 0.7105 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1
- Datasets 2.13.1
- Tokenizers 0.13.3
