multi_choice_bert-base-uncased_swag_finetune

This model is a fine-tuned version of bert-base-uncased on the swag dataset. It achieves the following results on the evaluation set:

• Loss: 0.7488
• Accuracy: 0.8004

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: 32
• eval_batch_size: 32
• 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
0.7512	1.0	2299	0.5688	0.7868
0.3857	2.0	4598	0.5583	0.7983
0.1556	3.0	6897	0.7488	0.8004

Framework versions

• Transformers 4.29.2
• Pytorch 2.0.1+cu117
• Datasets 2.12.0
• Tokenizers 0.13.3