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gpt2-cl-length-sampling
NasimB/gpt2-cl-length-sampling
gpt2-cl-length-sampling is a text generation model from NasimB. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Generation
- Library
- transformers
- License
- mit
- Model type
- gpt2
- Access
- Public
- Created
- Jun 30, 2023
- Updated
- Jun 30, 2023
- SHA
- 8b15b521
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Datasets
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
gpt2-cl-length-sampling
This model is a fine-tuned version of gpt2 on the generator dataset. It achieves the following results on the evaluation set:
- Loss: 5.0276
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: 1
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 6.4896 | 0.1 | 500 | 5.9335 |
| 5.1803 | 0.2 | 1000 | 5.5576 |
| 4.871 | 0.3 | 1500 | 5.3672 |
| 4.6547 | 0.4 | 2000 | 5.2453 |
| 4.5086 | 0.5 | 2500 | 5.1611 |
| 4.3642 | 0.6 | 3000 | 5.0948 |
| 4.2412 | 0.7 | 3500 | 5.0350 |
| 4.1326 | 0.8 | 4000 | 4.9978 |
| 4.0612 | 0.9 | 4500 | 4.9717 |
| 4.0255 | 1.0 | 5000 | 4.9665 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3
- Task
- Text Generation
- Library
- transformers
- Type
- gpt2
- License
- mit
- Created
- Jun 30, 2023
- Updated
- Jun 30, 2023