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