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gpt2-concat-cbt-mod-formatting-rarity-all-no-cut
This model is a fine-tuned version of gpt2 on the generator dataset. It achieves the following results on the evaluation set:
- Loss: 4.3193
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: 0.0005
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 6
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 6.6908 | 0.29 | 500 | 5.6370 |
| 5.3424 | 0.58 | 1000 | 5.2171 |
| 4.9975 | 0.87 | 1500 | 4.9619 |
| 4.7162 | 1.17 | 2000 | 4.8140 |
| 4.5583 | 1.46 | 2500 | 4.6912 |
| 4.4472 | 1.75 | 3000 | 4.5875 |
| 4.3333 | 2.04 | 3500 | 4.5046 |
| 4.1323 | 2.33 | 4000 | 4.4556 |
| 4.0941 | 2.62 | 4500 | 4.3964 |
| 4.0666 | 2.92 | 5000 | 4.3435 |
| 3.8641 | 3.21 | 5500 | 4.3427 |
| 3.7979 | 3.5 | 6000 | 4.3080 |
| 3.7842 | 3.79 | 6500 | 4.2744 |
| 3.6961 | 4.08 | 7000 | 4.2720 |
| 3.5126 | 4.37 | 7500 | 4.2677 |
| 3.5148 | 4.66 | 8000 | 4.2523 |
| 3.4967 | 4.96 | 8500 | 4.2389 |
| 3.3496 | 5.25 | 9000 | 4.2522 |
| 3.3196 | 5.54 | 9500 | 4.2514 |
| 3.3175 | 5.83 | 10000 | 4.2504 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3
