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gpt2-dp-mod-datasets
This model is a fine-tuned version of gpt2 on the generator dataset. It achieves the following results on the evaluation set:
- Loss: 3.1587
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 |
|---|---|---|---|
| 6.721 | 0.28 | 500 | 5.6661 |
| 5.3704 | 0.55 | 1000 | 5.2444 |
| 5.0331 | 0.83 | 1500 | 4.9898 |
| 4.784 | 1.1 | 2000 | 4.8409 |
| 4.6004 | 1.38 | 2500 | 4.7323 |
| 4.5032 | 1.65 | 3000 | 4.6355 |
| 4.4157 | 1.93 | 3500 | 4.5419 |
| 4.2123 | 2.2 | 4000 | 4.5062 |
| 4.1323 | 2.48 | 4500 | 4.4562 |
| 4.1086 | 2.75 | 5000 | 4.3991 |
| 4.0432 | 3.03 | 5500 | 4.3667 |
| 3.8085 | 3.3 | 6000 | 4.3636 |
| 3.8151 | 3.58 | 6500 | 4.3268 |
| 3.7855 | 3.85 | 7000 | 4.2969 |
| 3.6519 | 4.13 | 7500 | 4.3076 |
| 3.5149 | 4.4 | 8000 | 4.3007 |
| 3.5086 | 4.68 | 8500 | 4.2851 |
| 3.4995 | 4.95 | 9000 | 4.2743 |
| 3.3468 | 5.23 | 9500 | 4.2884 |
| 3.3143 | 5.5 | 10000 | 4.2904 |
| 3.3138 | 5.78 | 10500 | 4.2893 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3
