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distilbart-xsum-6-6-finetuned-bbc-news-on-extractive
This model is a fine-tuned version of sshleifer/distilbart-xsum-6-6 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.5869
- Rouge1: 39.4885
- Rouge2: 31.7487
- Rougel: 31.9013
- Rougelsum: 34.0825
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: 5.6e-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
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|---|---|---|---|---|---|---|---|
| 1.4649 | 1.0 | 445 | 1.5047 | 39.1053 | 31.6651 | 32.3242 | 33.9332 |
| 1.2224 | 2.0 | 890 | 1.4986 | 39.4115 | 31.7894 | 32.1057 | 34.0454 |
| 1.0099 | 3.0 | 1335 | 1.5322 | 39.5936 | 31.9984 | 32.2283 | 34.1798 |
| 0.8687 | 4.0 | 1780 | 1.5869 | 39.4885 | 31.7487 | 31.9013 | 34.0825 |
Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
