hwaQing/distilbert-base-uncased-finetuned-mrpc-test

distilbert-base-uncased-finetuned-mrpc

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

• Loss: 0.5708
• Accuracy: 0.7034
• F1: 0.8207

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: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
No log	1.0	58	0.5708	0.7034	0.8207

Framework versions

• Transformers 4.10.2
• Pytorch 1.9.0+cu102
• Datasets 1.11.0
• Tokenizers 0.10.3