finetune-newwiki-summarization-ver2
This model is a fine-tuned version of minnehwg/finetune-newwiki-summarization-ver1 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4697
- Rouge1: 48.1659
- Rouge2: 25.1491
- Rougel: 34.7794
- Rougelsum: 37.0893
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: 1e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 7
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|---|---|---|---|---|---|---|---|
| 0.4912 | 1.0 | 990 | 0.4701 | 48.1754 | 25.0221 | 34.7613 | 37.0734 |
| 0.4748 | 2.0 | 1980 | 0.4694 | 48.3629 | 25.3649 | 35.0239 | 37.3084 |
| 0.4755 | 3.0 | 2970 | 0.4695 | 48.2770 | 25.1907 | 34.8456 | 37.1930 |
| 0.4703 | 4.0 | 3960 | 0.4696 | 48.1801 | 25.1769 | 34.8004 | 37.0817 |
| 0.468 | 5.0 | 4950 | 0.4697 | 48.1659 | 25.1491 | 34.7794 | 37.0893 |
Framework versions
- Transformers 4.17.0
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
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