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gpt2-concat-mod-datasets-rarity1-rarity-all-13k-2p6k
This model is a fine-tuned version of gpt2 on the generator dataset. It achieves the following results on the evaluation set:
- Loss: 3.2382
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: 7
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 6.7973 | 0.32 | 500 | 5.8474 |
| 5.4953 | 0.65 | 1000 | 5.4602 |
| 5.1505 | 0.97 | 1500 | 5.2610 |
| 4.8711 | 1.29 | 2000 | 5.1460 |
| 4.7547 | 1.61 | 2500 | 5.0485 |
| 4.6592 | 1.94 | 3000 | 4.9997 |
| 4.4552 | 2.26 | 3500 | 4.9771 |
| 4.4024 | 2.58 | 4000 | 4.9469 |
| 4.3565 | 2.91 | 4500 | 4.8791 |
| 4.1703 | 3.23 | 5000 | 4.9096 |
| 4.1146 | 3.55 | 5500 | 4.8802 |
| 4.097 | 3.88 | 6000 | 4.8532 |
| 3.9182 | 4.2 | 6500 | 4.8784 |
| 3.8312 | 4.52 | 7000 | 4.8790 |
| 3.8217 | 4.84 | 7500 | 4.8563 |
| 3.6814 | 5.17 | 8000 | 4.8842 |
| 3.5716 | 5.49 | 8500 | 4.9002 |
| 3.563 | 5.81 | 9000 | 4.8909 |
| 3.4914 | 6.14 | 9500 | 4.9122 |
| 3.407 | 6.46 | 10000 | 4.9184 |
| 3.4075 | 6.78 | 10500 | 4.9186 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3
