OntoMedQA
This model is a fine-tuned version of bert-base-uncased on the medmcqa dataset. It achieves the following results on the evaluation set:
- Loss: 1.2874
- Accuracy: 0.4118
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: 4
- eval_batch_size: 4
- 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 |
|---|---|---|---|---|
| No log | 1.0 | 187 | 1.2418 | 0.2941 |
| No log | 2.0 | 374 | 1.1449 | 0.4706 |
| 0.8219 | 3.0 | 561 | 1.2874 | 0.4118 |
Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1