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bert-large-uncased_stereoset_finetuned
This model is a fine-tuned version of bert-large-uncased on the stereoset dataset. It achieves the following results on the evaluation set:
- Loss: 1.0729
- Accuracy: 0.7716
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: 128
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 0.21 | 5 | 0.6925 | 0.5071 |
| No log | 0.42 | 10 | 0.6978 | 0.5008 |
| No log | 0.62 | 15 | 0.6891 | 0.5275 |
| No log | 0.83 | 20 | 0.6850 | 0.5487 |
| No log | 1.04 | 25 | 0.7521 | 0.5126 |
| No log | 1.25 | 30 | 0.6577 | 0.6177 |
| No log | 1.46 | 35 | 0.6759 | 0.5440 |
| No log | 1.67 | 40 | 0.6395 | 0.6405 |
| No log | 1.88 | 45 | 0.6064 | 0.6719 |
| No log | 2.08 | 50 | 0.5822 | 0.6986 |
| No log | 2.29 | 55 | 0.5566 | 0.7096 |
| No log | 2.5 | 60 | 0.5411 | 0.7331 |
| No log | 2.71 | 65 | 0.5448 | 0.7551 |
| No log | 2.92 | 70 | 0.5384 | 0.7339 |
| No log | 3.12 | 75 | 0.5487 | 0.7535 |
| No log | 3.33 | 80 | 0.5572 | 0.7567 |
| No log | 3.54 | 85 | 0.5763 | 0.7614 |
| No log | 3.75 | 90 | 0.5756 | 0.7645 |
| No log | 3.96 | 95 | 0.5524 | 0.7645 |
| No log | 4.17 | 100 | 0.6320 | 0.7614 |
| No log | 4.38 | 105 | 0.6512 | 0.7575 |
| No log | 4.58 | 110 | 0.6582 | 0.7606 |
| No log | 4.79 | 115 | 0.6731 | 0.7669 |
| No log | 5.0 | 120 | 0.6944 | 0.7575 |
| No log | 5.21 | 125 | 0.7142 | 0.7575 |
| No log | 5.42 | 130 | 0.7004 | 0.7645 |
| No log | 5.62 | 135 | 0.6794 | 0.7630 |
| No log | 5.83 | 140 | 0.7108 | 0.7606 |
| No log | 6.04 | 145 | 0.7730 | 0.7590 |
| No log | 6.25 | 150 | 0.8083 | 0.7614 |
| No log | 6.46 | 155 | 0.8361 | 0.7653 |
| No log | 6.67 | 160 | 0.8498 | 0.7692 |
| No log | 6.88 | 165 | 0.8769 | 0.7700 |
| No log | 7.08 | 170 | 0.8324 | 0.7582 |
| No log | 7.29 | 175 | 0.7945 | 0.7645 |
| No log | 7.5 | 180 | 0.8480 | 0.7684 |
| No log | 7.71 | 185 | 0.8905 | 0.7724 |
| No log | 7.92 | 190 | 0.9560 | 0.7700 |
| No log | 8.12 | 195 | 0.9976 | 0.7669 |
| No log | 8.33 | 200 | 1.0315 | 0.7677 |
| No log | 8.54 | 205 | 1.0413 | 0.7692 |
| No log | 8.75 | 210 | 1.0216 | 0.7708 |
| No log | 8.96 | 215 | 1.0251 | 0.7716 |
| No log | 9.17 | 220 | 1.0483 | 0.7716 |
| No log | 9.38 | 225 | 1.0616 | 0.7716 |
| No log | 9.58 | 230 | 1.0703 | 0.7708 |
| No log | 9.79 | 235 | 1.0731 | 0.7732 |
| No log | 10.0 | 240 | 1.0729 | 0.7716 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1
- Datasets 2.9.0
- Tokenizers 0.13.2
