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text_shortening_model_v50
This model is a fine-tuned version of facebook/bart-large-xsum on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.8296
- Rouge1: 0.5063
- Rouge2: 0.2803
- Rougel: 0.4415
- Rougelsum: 0.4405
- Bert precision: 0.8741
- Bert recall: 0.8787
- Average word count: 8.7857
- Max word count: 16
- Min word count: 3
- Average token count: 16.3942
- % shortened texts with length > 12: 11.9048
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: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- 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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Bert precision | Bert recall | Average word count | Max word count | Min word count | Average token count | % shortened texts with length > 12 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1.3343 | 1.0 | 83 | 1.4625 | 0.5101 | 0.2866 | 0.4536 | 0.4527 | 0.8751 | 0.877 | 8.3042 | 19 | 4 | 15.1508 | 5.0265 |
| 0.7011 | 2.0 | 166 | 1.4296 | 0.5101 | 0.284 | 0.4548 | 0.4551 | 0.8736 | 0.8797 | 8.7593 | 18 | 5 | 16.0529 | 7.672 |
| 0.483 | 3.0 | 249 | 1.3880 | 0.5025 | 0.2819 | 0.4433 | 0.442 | 0.8722 | 0.8782 | 8.7698 | 18 | 5 | 14.8492 | 6.3492 |
| 0.3876 | 4.0 | 332 | 1.7614 | 0.4934 | 0.2653 | 0.4334 | 0.4327 | 0.8715 | 0.8725 | 8.2249 | 18 | 5 | 16.3042 | 5.5556 |
| 0.291 | 5.0 | 415 | 1.8296 | 0.5063 | 0.2803 | 0.4415 | 0.4405 | 0.8741 | 0.8787 | 8.7857 | 16 | 3 | 16.3942 | 11.9048 |
Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
