bert-base-uncased-google-boolq

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Bert Base Uncased Boolean Question Answer model

This model is a fine-tuned version of bert-base-uncased on the boolq dataset. It achieves the following results on the evaluation set:

• Loss: 0.1993
• Accuracy: 0.7150

Model description

• Model type: Text Classification model
• Language(s) (NLP): English
• License: Apache 2.0

Intended uses & limitations

More information needed

Training and evaluation data

• Dataset

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 64
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 4

Training results

Framework versions

• Transformers 4.40.0
• Pytorch 2.2.2+cu121
• Datasets 2.19.0
• Tokenizers 0.19.1