bert-large-cased-mnli-model2

This model is a fine-tuned version of bert-large-cased on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.4791
• Accuracy: 0.8540

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: 64
• eval_batch_size: 64
• seed: 25
• 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
0.4183	1.0	6136	0.3858	0.8511
0.2906	2.0	12272	0.4071	0.8584
0.175	3.0	18408	0.4791	0.8540

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.1+cu121
• Datasets 2.15.0
• Tokenizers 0.15.0