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text_shortening_model_v60
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: 0.7251
- Rouge1: 0.7246
- Rouge2: 0.5572
- Rougel: 0.6745
- Rougelsum: 0.6724
- Bert precision: 0.9227
- Bert recall: 0.9242
- Bert f1-score: 0.923
- Average word count: 8.4018
- Max word count: 16
- Min word count: 4
- Average token count: 16.1562
- % shortened texts with length > 12: 7.5893
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: 1e-05
- 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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bert precision | Bert recall | Bert f1-score | Average word count | Max word count | Min word count | Average token count | % shortened texts with length > 12 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1.4241 | 1.0 | 49 | 0.7533 | 0.7094 | 0.5458 | 0.6655 | 0.6641 | 0.9182 | 0.9214 | 0.9193 | 8.3884 | 17 | 5 | 15.3661 | 6.25 |
| 0.5792 | 2.0 | 98 | 0.7279 | 0.7058 | 0.5397 | 0.6587 | 0.6582 | 0.9201 | 0.9193 | 0.9192 | 8.3393 | 17 | 4 | 15.9062 | 5.3571 |
| 0.4392 | 3.0 | 147 | 0.7251 | 0.7246 | 0.5572 | 0.6745 | 0.6724 | 0.9227 | 0.9242 | 0.923 | 8.4018 | 16 | 4 | 16.1562 | 7.5893 |
Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
