distilhubert-ft-common-language
This model is a fine-tuned version of ntu-spml/distilhubert on the common_language dataset. It achieves the following results on the evaluation set:
- Loss: 2.7214
- Accuracy: 0.2797
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: 3e-05
- train_batch_size: 32
- eval_batch_size: 4
- seed: 0
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 3.6543 | 1.0 | 173 | 3.7611 | 0.0491 |
| 3.2221 | 2.0 | 346 | 3.4868 | 0.1352 |
| 2.9332 | 3.0 | 519 | 3.2732 | 0.1861 |
| 2.7299 | 4.0 | 692 | 3.0944 | 0.2172 |
| 2.5638 | 5.0 | 865 | 2.9790 | 0.2400 |
| 2.3871 | 6.0 | 1038 | 2.8668 | 0.2590 |
| 2.3384 | 7.0 | 1211 | 2.7972 | 0.2653 |
| 2.2648 | 8.0 | 1384 | 2.7625 | 0.2695 |
| 2.2162 | 9.0 | 1557 | 2.7405 | 0.2782 |
| 2.1915 | 10.0 | 1730 | 2.7214 | 0.2797 |
Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.9.1+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3