bert_uncased_L-2_H-256_A-4_massive
This model is a fine-tuned version of google/bert_uncased_L-2_H-256_A-4 on the massive dataset. It achieves the following results on the evaluation set:
- Loss: 0.8268
- Accuracy: 0.8062
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: 64
- eval_batch_size: 64
- seed: 33
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 3.6523 | 1.0 | 180 | 3.0957 | 0.3114 |
| 2.7875 | 2.0 | 360 | 2.3220 | 0.5352 |
| 2.1742 | 3.0 | 540 | 1.8439 | 0.6483 |
| 1.7765 | 4.0 | 720 | 1.5345 | 0.6940 |
| 1.4988 | 5.0 | 900 | 1.3275 | 0.7137 |
| 1.3009 | 6.0 | 1080 | 1.1805 | 0.7368 |
| 1.1512 | 7.0 | 1260 | 1.0746 | 0.7511 |
| 1.0374 | 8.0 | 1440 | 0.9977 | 0.7649 |
| 0.9466 | 9.0 | 1620 | 0.9426 | 0.7757 |
| 0.8821 | 10.0 | 1800 | 0.8991 | 0.7909 |
| 0.828 | 11.0 | 1980 | 0.8648 | 0.7929 |
| 0.7824 | 12.0 | 2160 | 0.8426 | 0.7988 |
| 0.7565 | 13.0 | 2340 | 0.8268 | 0.8062 |
| 0.7378 | 14.0 | 2520 | 0.8180 | 0.8052 |
| 0.7231 | 15.0 | 2700 | 0.8142 | 0.8047 |
Framework versions
- Transformers 4.34.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.14.5
- Tokenizers 0.14.1