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
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
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.2317 | 0.9966 | 147 | 0.2198 | 0.6569 |
| 0.2 | 2.0 | 295 | 0.2002 | 0.6960 |
| 0.1741 | 2.9966 | 442 | 0.1968 | 0.7122 |
| 0.1469 | 3.9864 | 588 | 0.1993 | 0.7150 |
Framework versions
- Transformers 4.40.0
- Pytorch 2.2.2+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1