gpt2-summarization_reward_model
This model is a fine-tuned version of gpt2 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.7473
- Accuracy: 0.6006
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: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 16
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy |
---|---|---|---|---|
0.6421 | 1.0 | 1451 | 0.6815 | 0.6036 |
0.5893 | 2.0 | 2902 | 0.6764 | 0.6048 |
0.5488 | 3.0 | 4353 | 0.7074 | 0.6012 |
0.5187 | 4.0 | 5804 | 0.7254 | 0.6009 |
0.5034 | 5.0 | 7255 | 0.7473 | 0.6006 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2
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