bert-base-multilingual-cased-mrpc-1

This model is a fine-tuned version of bert-base-multilingual-cased on the tmnam20/VieGLUE/MRPC dataset. It achieves the following results on the evaluation set:

• Loss: 0.3289
• Accuracy: 0.8529
• F1: 0.8885
• Combined Score: 0.8707

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: 32
• eval_batch_size: 16
• seed: 1
• 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 4.35.2
• Pytorch 2.2.0.dev20231203+cu121
• Datasets 2.15.0
• Tokenizers 0.15.0