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
Downloads last month
2
Hosted inference API

Unable to determine this model’s pipeline type. Check the docs .