jxuhf/Fine-tuning-text-classification-model-Habana-Gaudi

mrpc

This model is a fine-tuned version of bert-large-uncased-whole-word-masking on the GLUE MRPC dataset. It achieves the following results on the evaluation set:

• Loss: 0.3680
• Accuracy: 0.8824
• F1: 0.9181
• Combined Score: 0.9002

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: 3e-05
• train_batch_size: 32
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06
• lr_scheduler_type: linear
• num_epochs: 3.0

Training results

Framework versions

• Transformers 4.18.0
• Pytorch 1.10.0a0+gitfe03f8c
• Datasets 2.1.0
• Tokenizers 0.12.1