distilhubert-ko-zeroth
This model is a fine-tuned version of ntu-spml/distilhubert on the BINGSU/ZEROTH-KOREAN - NA dataset. It achieves the following results on the evaluation set:
- Loss: 0.9934
- Cer: 0.2066
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: 0.001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 500
- num_epochs: 10.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|---|---|---|---|---|
| No log | 0.57 | 400 | 3.2681 | 0.6761 |
| 7.285 | 1.15 | 800 | 1.5312 | 0.4170 |
| 1.259 | 1.72 | 1200 | 1.3459 | 0.3846 |
| 0.9108 | 2.3 | 1600 | 1.1357 | 0.3239 |
| 0.7227 | 2.87 | 2000 | 1.0571 | 0.3056 |
| 0.7227 | 3.45 | 2400 | 1.0002 | 0.2829 |
| 0.5689 | 4.02 | 2800 | 0.8773 | 0.2553 |
| 0.4676 | 4.6 | 3200 | 0.8634 | 0.2462 |
| 0.3805 | 5.17 | 3600 | 0.8504 | 0.2323 |
| 0.2548 | 5.75 | 4000 | 0.8480 | 0.2260 |
| 0.2548 | 6.32 | 4400 | 0.8550 | 0.2231 |
| 0.189 | 6.9 | 4800 | 0.8587 | 0.2159 |
| 0.1336 | 7.47 | 5200 | 0.9012 | 0.2101 |
| 0.0827 | 8.05 | 5600 | 0.9302 | 0.2100 |
| 0.0506 | 8.62 | 6000 | 0.9622 | 0.2063 |
| 0.0506 | 9.2 | 6400 | 0.9826 | 0.2062 |
| 0.0389 | 9.77 | 6800 | 0.9933 | 0.2067 |
Framework versions
- Transformers 4.21.1
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1