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text_shortening_model_v15
This model is a fine-tuned version of t5-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.6707
- Rouge1: 0.5171
- Rouge2: 0.3003
- Rougel: 0.4648
- Rougelsum: 0.4666
- Bert precision: 0.8787
- Bert recall: 0.8819
- Average word count: 11.25
- Max word count: 18
- Min word count: 5
- Average token count: 16.35
- % shortened texts with length > 12: 36.4286
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1.1859 | 1.0 | 62 | 1.6707 | 0.5171 | 0.3003 | 0.4648 | 0.4666 | 0.8787 | 0.8819 | 11.25 | 18 | 5 | 16.35 | 36.4286 |
Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cpu
- Datasets 2.14.5
- Tokenizers 0.13.3
