DLL888/bert-base-uncased-squad
This model is a fine-tuned version of bert-base-uncased on SQuAD dataset. It achieves the following results on the evaluation set:
- Exact Match: 80.21759697256385
- F1: 87.77849998885436
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training Machine
Trained in Google Colab Pro with the following specs:
- A100-SXM4-40GB
- NVIDIA-SMI 460.32.03
- Driver Version: 460.32.03
- CUDA Version: 11.2
Training took about 26 minutes for two epochs.
Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 10564, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'passive_serialization': True}, 'warmup_steps': 500, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: mixed_float16
Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|---|---|---|---|---|---|---|
| 1.4348 | 0.6368 | 0.5974 | 1.0155 | 0.7193 | 0.6825 | 0 |
| 0.8072 | 0.7735 | 0.7320 | 0.9990 | 0.7302 | 0.6983 | 1 |
Framework versions
- Transformers 4.24.0
- TensorFlow 2.9.2
- Datasets 2.7.1
- Tokenizers 0.13.2