albert-base-v2-finetuned-filtered-0609
This model is a fine-tuned version of albert-base-v2 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2062
- Accuracy: 0.9723
- Precision: 0.9724
- Recall: 0.9723
- F1: 0.9723
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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.2688 | 1.0 | 3180 | 0.2282 | 0.9560 | 0.9577 | 0.9560 | 0.9562 |
| 0.2268 | 2.0 | 6360 | 0.1909 | 0.9638 | 0.9640 | 0.9638 | 0.9638 |
| 0.1831 | 3.0 | 9540 | 0.2590 | 0.9572 | 0.9584 | 0.9572 | 0.9572 |
| 0.1588 | 4.0 | 12720 | 0.1752 | 0.9673 | 0.9678 | 0.9673 | 0.9673 |
| 0.0972 | 5.0 | 15900 | 0.1868 | 0.9695 | 0.9696 | 0.9695 | 0.9695 |
| 0.0854 | 6.0 | 19080 | 0.2042 | 0.9701 | 0.9707 | 0.9701 | 0.9702 |
| 0.0599 | 7.0 | 22260 | 0.1793 | 0.9748 | 0.9749 | 0.9748 | 0.9749 |
| 0.0389 | 8.0 | 25440 | 0.1996 | 0.9742 | 0.9743 | 0.9742 | 0.9742 |
| 0.0202 | 9.0 | 28620 | 0.2188 | 0.9723 | 0.9726 | 0.9723 | 0.9724 |
| 0.0152 | 10.0 | 31800 | 0.2062 | 0.9723 | 0.9724 | 0.9723 | 0.9723 |
Framework versions
- Transformers 4.19.2
- Pytorch 1.9.1+cu111
- Datasets 1.16.1
- Tokenizers 0.12.1