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bert-large-uncased-sst-2-64-13-30
This model is a fine-tuned version of bert-large-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6366
- Accuracy: 0.8594
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: 1.5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- num_epochs: 30
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 4 | 0.6822 | 0.5781 |
| No log | 2.0 | 8 | 0.6592 | 0.6172 |
| 0.6625 | 3.0 | 12 | 0.6330 | 0.6719 |
| 0.6625 | 4.0 | 16 | 0.5916 | 0.75 |
| 0.5388 | 5.0 | 20 | 0.5256 | 0.8047 |
| 0.5388 | 6.0 | 24 | 0.4758 | 0.8125 |
| 0.5388 | 7.0 | 28 | 0.4535 | 0.8203 |
| 0.3481 | 8.0 | 32 | 0.4449 | 0.8203 |
| 0.3481 | 9.0 | 36 | 0.4246 | 0.8203 |
| 0.2064 | 10.0 | 40 | 0.3979 | 0.8359 |
| 0.2064 | 11.0 | 44 | 0.3869 | 0.8594 |
| 0.2064 | 12.0 | 48 | 0.3902 | 0.8672 |
| 0.1105 | 13.0 | 52 | 0.4068 | 0.8516 |
| 0.1105 | 14.0 | 56 | 0.4299 | 0.8516 |
| 0.0583 | 15.0 | 60 | 0.4556 | 0.8516 |
| 0.0583 | 16.0 | 64 | 0.4352 | 0.8594 |
| 0.0583 | 17.0 | 68 | 0.4515 | 0.8672 |
| 0.0313 | 18.0 | 72 | 0.4882 | 0.8672 |
| 0.0313 | 19.0 | 76 | 0.5242 | 0.8516 |
| 0.0201 | 20.0 | 80 | 0.5497 | 0.8594 |
| 0.0201 | 21.0 | 84 | 0.5667 | 0.8594 |
| 0.0201 | 22.0 | 88 | 0.5827 | 0.8594 |
| 0.0132 | 23.0 | 92 | 0.6062 | 0.8516 |
| 0.0132 | 24.0 | 96 | 0.6265 | 0.8516 |
| 0.017 | 25.0 | 100 | 0.6330 | 0.8438 |
| 0.017 | 26.0 | 104 | 0.6334 | 0.8516 |
| 0.017 | 27.0 | 108 | 0.6354 | 0.8516 |
| 0.0211 | 28.0 | 112 | 0.6361 | 0.8594 |
| 0.0211 | 29.0 | 116 | 0.6363 | 0.8594 |
| 0.0113 | 30.0 | 120 | 0.6366 | 0.8594 |
Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.4.0
- Tokenizers 0.13.3
