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bert-base-uncased-8-50-0.01
This model is a fine-tuned version of bert-base-uncased on the glue dataset. It achieves the following results on the evaluation set:
- Loss: 0.9219
- Matthews Correlation: 0.0
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: 0.01
- 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
- num_epochs: 50
Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|---|---|---|---|---|
| No log | 1.0 | 400 | 0.9219 | 0.0 |
| 1.2047 | 2.0 | 800 | 1.8168 | 0.0 |
| 1.0707 | 3.0 | 1200 | 1.4474 | 0.0 |
| 1.0538 | 4.0 | 1600 | 1.5223 | 0.0 |
| 1.316 | 5.0 | 2000 | 0.8467 | 0.0 |
| 1.316 | 6.0 | 2400 | 1.0906 | 0.0 |
| 1.2739 | 7.0 | 2800 | 0.6851 | 0.0 |
| 1.1342 | 8.0 | 3200 | 1.3170 | 0.0 |
| 1.2572 | 9.0 | 3600 | 0.8870 | 0.0 |
| 1.0237 | 10.0 | 4000 | 1.3236 | 0.0 |
| 1.0237 | 11.0 | 4400 | 0.9025 | 0.0 |
| 0.9597 | 12.0 | 4800 | 0.7757 | 0.0 |
| 1.0946 | 13.0 | 5200 | 1.2551 | 0.0 |
| 1.0011 | 14.0 | 5600 | 1.1606 | 0.0 |
| 1.1111 | 15.0 | 6000 | 0.6040 | 0.0 |
| 1.1111 | 16.0 | 6400 | 1.4347 | 0.0 |
| 1.0098 | 17.0 | 6800 | 0.6218 | 0.0 |
| 1.0829 | 18.0 | 7200 | 0.4979 | 0.0 |
| 0.9131 | 19.0 | 7600 | 1.3040 | 0.0 |
| 0.879 | 20.0 | 8000 | 2.0309 | 0.0 |
| 0.879 | 21.0 | 8400 | 0.5150 | 0.0 |
| 0.9646 | 22.0 | 8800 | 0.4850 | 0.0 |
| 0.9625 | 23.0 | 9200 | 0.5076 | 0.0 |
| 0.9129 | 24.0 | 9600 | 1.1277 | 0.0 |
| 0.8839 | 25.0 | 10000 | 0.9403 | 0.0 |
| 0.8839 | 26.0 | 10400 | 1.6226 | 0.0 |
| 0.9264 | 27.0 | 10800 | 0.6049 | 0.0 |
| 0.7999 | 28.0 | 11200 | 0.9549 | 0.0 |
| 0.752 | 29.0 | 11600 | 0.6757 | 0.0 |
| 0.7675 | 30.0 | 12000 | 0.7320 | 0.0 |
| 0.7675 | 31.0 | 12400 | 0.8393 | 0.0 |
| 0.6887 | 32.0 | 12800 | 0.5977 | 0.0 |
| 0.7563 | 33.0 | 13200 | 0.4815 | 0.0 |
| 0.7671 | 34.0 | 13600 | 0.5457 | 0.0 |
| 0.7227 | 35.0 | 14000 | 0.7384 | 0.0 |
| 0.7227 | 36.0 | 14400 | 0.7749 | 0.0 |
| 0.7308 | 37.0 | 14800 | 0.4726 | 0.0 |
| 0.7191 | 38.0 | 15200 | 0.5069 | 0.0 |
| 0.6846 | 39.0 | 15600 | 0.4762 | 0.0 |
| 0.6151 | 40.0 | 16000 | 0.4738 | 0.0 |
| 0.6151 | 41.0 | 16400 | 0.5114 | 0.0 |
| 0.5982 | 42.0 | 16800 | 0.4866 | 0.0 |
| 0.6199 | 43.0 | 17200 | 0.4717 | 0.0 |
| 0.5737 | 44.0 | 17600 | 0.7651 | 0.0 |
| 0.5703 | 45.0 | 18000 | 0.8008 | 0.0 |
| 0.5703 | 46.0 | 18400 | 0.5391 | 0.0 |
| 0.5748 | 47.0 | 18800 | 0.5097 | 0.0 |
| 0.5297 | 48.0 | 19200 | 0.4731 | 0.0 |
| 0.4902 | 49.0 | 19600 | 0.4720 | 0.0 |
| 0.4955 | 50.0 | 20000 | 0.4748 | 0.0 |
Framework versions
- Transformers 4.16.2
- Pytorch 1.9.0
- Datasets 1.18.3
- Tokenizers 0.11.0
