albert-base-v2-finetuned-qnli
This model is a fine-tuned version of albert-base-v2 on the glue dataset. It achieves the following results on the evaluation set:
- Loss: 0.3194
- Accuracy: 0.9112
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.3116 | 1.0 | 6547 | 0.2818 | 0.8849 |
| 0.2467 | 2.0 | 13094 | 0.2532 | 0.9001 |
| 0.1858 | 3.0 | 19641 | 0.3194 | 0.9112 |
| 0.1449 | 4.0 | 26188 | 0.4338 | 0.9103 |
| 0.0584 | 5.0 | 32735 | 0.5752 | 0.9052 |
Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.0
- Tokenizers 0.10.3
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