finetuned-bert-piqa

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

• Loss: 0.6603
• Accuracy: 0.6518

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: 3e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 8
• total_train_batch_size: 64
• 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	251	0.6751	0.6115
0.6628	2.0	502	0.6556	0.6534
0.6628	3.0	753	0.6603	0.6518

Framework versions

• Transformers 4.20.1
• Pytorch 1.11.0+cu113
• Datasets 2.3.2
• Tokenizers 0.12.1