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albert-base-v2-finetuned-squad-v2
This model is a fine-tuned version of albert-base-v2 on the squad_v2 dataset. It achieves the following results on the evaluation set:
- Loss: 0.9645
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.864 | 1.0 | 8248 | 0.8698 |
| 0.6246 | 2.0 | 16496 | 0.8351 |
| 0.4359 | 3.0 | 24744 | 0.9645 |
Performance
- 'exact': 78.36267160784975,
- 'f1': 81.72483834090231,
- 'total': 11873,
- 'HasAns_exact': 74.527665317139,
- 'HasAns_f1': 81.26164062441536,
- 'HasAns_total': 5928,
- 'NoAns_exact': 82.18671152228764,
- 'NoAns_f1': 82.18671152228764,
- 'NoAns_total': 5945,
- 'best_exact': 78.36267160784975,
- 'best_exact_thresh': 0.9990501403808594,
- 'best_f1': 81.72483834090268,
- 'best_f1_thresh': 0.9990501403808594,
- 'total_time_in_seconds': 224.37217425400013,
- 'samples_per_second': 52.9165438605555,
- 'latency_in_seconds': 0.018897681651983505
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
