roberta-large-finetuned-csqa

This model is a fine-tuned version of roberta-large on the commonsense_qa dataset. It achieves the following results on the evaluation set:

• Loss: 0.9146
• Accuracy: 0.7330

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: 1e-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: 5
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.3903	1.0	609	0.8845	0.6642
0.8939	2.0	1218	0.7054	0.7281
0.6163	3.0	1827	0.7452	0.7314
0.4245	4.0	2436	0.8369	0.7355
0.3258	5.0	3045	0.9146	0.7330

Framework versions

• Transformers 4.9.0
• Pytorch 1.9.0
• Datasets 1.10.2
• Tokenizers 0.10.3