SQuAD-extractive_QA-bert-base-cased

This model is a fine-tuned version of bert-base-cased on the SQuAD dataset.

Model description

bert-base-cased

Intended uses & limitations

Extractive QA

Training and evaluation data

SQuAD dataset. Can be accessed using the load_dataset() method.

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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 3
• mixed_precision_training: Native AMP

Training results

After training, exact_match: 80.246, and f1: 87.665

Framework versions

• Transformers 4.50.0
• Pytorch 2.6.0+cu124
• Datasets 3.5.0
• Tokenizers 0.21.1