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BERT_MC_OpenBookQA_w_wrong_context
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.7450
- Accuracy: 0.922
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: 11
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.3525 | 1.0 | 1859 | 0.2696 | 0.906 |
| 0.2084 | 2.0 | 3718 | 0.3284 | 0.9143 |
| 0.1263 | 3.0 | 5577 | 0.4205 | 0.9143 |
| 0.0734 | 4.0 | 7436 | 0.4688 | 0.9203 |
| 0.0437 | 5.0 | 9295 | 0.6266 | 0.9173 |
| 0.0357 | 6.0 | 11154 | 0.6934 | 0.9207 |
| 0.0264 | 7.0 | 13013 | 0.6947 | 0.92 |
| 0.0098 | 8.0 | 14872 | 0.6800 | 0.9197 |
| 0.0104 | 9.0 | 16731 | 0.7393 | 0.923 |
| 0.0067 | 10.0 | 18590 | 0.7846 | 0.9217 |
| 0.0034 | 11.0 | 20449 | 0.7450 | 0.922 |
Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1
- Datasets 2.5.1
- Tokenizers 0.11.0
