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distilbart-cnn-12-6-ftn-multi_news
This model is a fine-tuned version of sshleifer/distilbart-cnn-12-6 on the multi_news dataset. It achieves the following results on the evaluation set:
- Loss: 3.8143
- Rouge1: 41.6136
- Rouge2: 14.7454
- Rougel: 23.3597
- Rougelsum: 36.1973
- Gen Len: 130.874
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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
- mixed_precision_training: Native AMP
- label_smoothing_factor: 0.1
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 3.8821 | 0.89 | 2000 | 3.8143 | 41.6136 | 14.7454 | 23.3597 | 36.1973 | 130.874 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
