bert-base-uncased-with-misspellings-correction

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

• Loss: 0.2307
• Accuracy: 0.9023
• Precision: 0.9090
• Recall: 0.9023
• F1: 0.9045
• Balanced Acc: 0.8858

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: 32
• seed: 0
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 1

Training results

Framework versions

• Transformers 4.29.2
• Pytorch 2.0.0
• Datasets 2.1.0
• Tokenizers 0.13.3