qa_model
This model is a fine-tuned version of google-bert/bert-base-uncased on the swag dataset. It achieves the following results on the evaluation set:
- Loss: 1.0121
- 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: 5e-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 |
|---|---|---|---|---|
| 0.7622 | 1.0 | 4597 | 0.6089 | 0.7635 |
| 0.3862 | 2.0 | 9194 | 0.6362 | 0.7888 |
| 0.1407 | 3.0 | 13791 | 1.0121 | 0.7917 |
Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
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Base model
google-bert/bert-base-uncased