bert-tiny-emotion-KD-distilBERT
This model is a fine-tuned version of google/bert_uncased_L-2_H-128_A-2 on the emotion dataset. It achieves the following results on the evaluation set:
- Loss: 0.5444
- Accuracy: 0.913
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: 16
- eval_batch_size: 16
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 4.2533 | 1.0 | 1000 | 2.8358 | 0.7675 |
| 2.3274 | 2.0 | 2000 | 1.5893 | 0.8675 |
| 1.3974 | 3.0 | 3000 | 1.0286 | 0.891 |
| 0.9035 | 4.0 | 4000 | 0.7534 | 0.8955 |
| 0.6619 | 5.0 | 5000 | 0.6350 | 0.905 |
| 0.5482 | 6.0 | 6000 | 0.6180 | 0.899 |
| 0.4937 | 7.0 | 7000 | 0.5448 | 0.91 |
| 0.4013 | 8.0 | 8000 | 0.5493 | 0.906 |
| 0.3839 | 9.0 | 9000 | 0.5481 | 0.9095 |
| 0.3281 | 10.0 | 10000 | 0.5528 | 0.9115 |
| 0.3098 | 11.0 | 11000 | 0.5864 | 0.9095 |
| 0.2762 | 12.0 | 12000 | 0.5566 | 0.9095 |
| 0.2467 | 13.0 | 13000 | 0.5444 | 0.913 |
| 0.2286 | 14.0 | 14000 | 0.5306 | 0.912 |
| 0.2215 | 15.0 | 15000 | 0.5312 | 0.9115 |
| 0.2038 | 16.0 | 16000 | 0.5242 | 0.912 |
Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1