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distilbart-xsum-6-6-finetuned-bbc-news-on-abstractive
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: 1.6549
- Rouge1: 38.9186
- Rouge2: 30.2223
- Rougel: 32.6201
- Rougelsum: 37.7502
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 |
|---|---|---|---|---|---|---|---|
| 1.3838 | 1.0 | 445 | 1.4841 | 39.1621 | 30.4379 | 32.6613 | 37.9963 |
| 1.0077 | 2.0 | 890 | 1.5173 | 39.388 | 30.9125 | 33.099 | 38.2442 |
| 0.7983 | 3.0 | 1335 | 1.5726 | 38.7913 | 30.0766 | 32.6092 | 37.5953 |
| 0.6681 | 4.0 | 1780 | 1.6549 | 38.9186 | 30.2223 | 32.6201 | 37.7502 |
Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
