bert-base-uncased-finetuned-swag

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

• eval_loss: 0.6189
• eval_accuracy: 0.7647
• eval_runtime: 274.5502
• eval_samples_per_second: 72.868
• eval_steps_per_second: 4.557
• epoch: 1.0
• step: 4597

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

Framework versions

• Transformers 4.9.1
• Pytorch 1.9.0+cu102
• Datasets 1.11.0
• Tokenizers 0.10.3