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DLL888/deberta-v3-base-squad
This model is a fine-tuned version of microsoft/deberta-v3-base on the SQuAD dataset.
It achieves the following results on the evaluation set:
- Exact Match: 88.08893093661305
- F1: 93.75543944888847
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': 10538, '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: float32
Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|---|---|---|---|---|---|---|
| 1.0540 | 0.7261 | 0.6885 | 0.7617 | 0.7841 | 0.7530 | 0 |
| 0.6248 | 0.8212 | 0.7777 | 0.7594 | 0.7873 | 0.7569 | 1 |
Framework versions
- Transformers 4.24.0
- TensorFlow 2.9.2
- Datasets 2.7.1
- Tokenizers 0.13.2
