bert_finetuning_test

This model is a fine-tuned version of google-bert/bert-base-cased on the GLUE MRPC dataset. It achieves the following results on the evaluation set:

• Loss: 0.7261
• Accuracy: 0.8309
• F1: 0.8812
• Combined Score: 0.8561

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: 5e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 3.0

Training results

Framework versions

• Transformers 4.41.0.dev0
• Pytorch 2.3.0+cpu
• Datasets 2.19.0
• Tokenizers 0.19.1