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gpt2-dp-mod-datasets-txt-processing-rarity-all
This model is a fine-tuned version of gpt2 on the generator dataset. It achieves the following results on the evaluation set:
- Loss: 4.4242
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 |
---|---|---|---|
6.7606 | 0.29 | 500 | 5.6933 |
5.4375 | 0.59 | 1000 | 5.2559 |
5.0937 | 0.88 | 1500 | 5.0171 |
4.8204 | 1.18 | 2000 | 4.8701 |
4.6728 | 1.47 | 2500 | 4.7593 |
4.574 | 1.77 | 3000 | 4.6587 |
4.4456 | 2.06 | 3500 | 4.5885 |
4.258 | 2.36 | 4000 | 4.5468 |
4.2423 | 2.65 | 4500 | 4.4860 |
4.2036 | 2.94 | 5000 | 4.4302 |
3.9737 | 3.24 | 5500 | 4.4364 |
3.9439 | 3.53 | 6000 | 4.4019 |
3.9271 | 3.83 | 6500 | 4.3632 |
3.7901 | 4.12 | 7000 | 4.3689 |
3.6474 | 4.42 | 7500 | 4.3662 |
3.6414 | 4.71 | 8000 | 4.3472 |
3.6338 | 5.01 | 8500 | 4.3344 |
3.3764 | 5.3 | 9000 | 4.3618 |
3.3821 | 5.59 | 9500 | 4.3568 |
3.3777 | 5.89 | 10000 | 4.3513 |
3.2752 | 6.18 | 10500 | 4.3602 |
3.2228 | 6.48 | 11000 | 4.3652 |
3.2172 | 6.77 | 11500 | 4.3656 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3