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text_shortening_model_v49
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: 1.7760
- Rouge1: 0.5119
- Rouge2: 0.2768
- Rougel: 0.4448
- Rougelsum: 0.4444
- Bert precision: 0.8755
- Bert recall: 0.8801
- Average word count: 8.8492
- Max word count: 20
- Min word count: 5
- Average token count: 16.4709
- % shortened texts with length > 12: 8.7302
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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1.8542 | 1.0 | 83 | 1.6189 | 0.5121 | 0.2699 | 0.4302 | 0.4304 | 0.863 | 0.8909 | 11.3386 | 21 | 5 | 19.4312 | 31.746 |
| 0.9651 | 2.0 | 166 | 1.4837 | 0.4957 | 0.2664 | 0.4347 | 0.4362 | 0.8687 | 0.8758 | 8.8598 | 19 | 4 | 16.9815 | 9.2593 |
| 0.608 | 3.0 | 249 | 1.4074 | 0.5012 | 0.2693 | 0.4346 | 0.4342 | 0.8725 | 0.8781 | 8.836 | 20 | 4 | 15.5265 | 5.5556 |
| 0.3788 | 4.0 | 332 | 1.5646 | 0.5202 | 0.2836 | 0.4535 | 0.4537 | 0.876 | 0.881 | 8.9312 | 18 | 5 | 16.4365 | 10.3175 |
| 0.2296 | 5.0 | 415 | 1.7760 | 0.5119 | 0.2768 | 0.4448 | 0.4444 | 0.8755 | 0.8801 | 8.8492 | 20 | 5 | 16.4709 | 8.7302 |
Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
