bert-large-cased-finetuned-wnli

This model is a fine-tuned version of bert-large-cased on the GLUE WNLI dataset. It achieves the following results on the evaluation set:

• Loss: 0.7087
• Accuracy: 0.3521

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: 2e-05
• train_batch_size: 4
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 5.0

Training results

Training Loss	Epoch	Step	Accuracy	Validation Loss
0.7114	1.0	159	0.5634	0.6923
0.7141	2.0	318	0.5634	0.6895
0.7063	3.0	477	0.5634	0.6930
0.712	4.0	636	0.4507	0.7077
0.7037	5.0	795	0.3521	0.7087

Framework versions

• Transformers 4.11.0.dev0
• Pytorch 1.9.0
• Datasets 1.12.1
• Tokenizers 0.10.3