<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->
text_shortening_model_v18
This model is a fine-tuned version of t5-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.7863
- Rouge1: 0.6984
- Rouge2: 0.3313
- Rougel: 0.4652
- Rougelsum: 0.6832
- Bert precision: 0.8799
- Bert recall: 0.8838
- Average word count: 1610.0
- Max word count: 1610
- Min word count: 1610
- Average token count: 16.8143
- % shortened texts with length > 12: 100.0
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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1.195 | 1.0 | 62 | 1.7863 | 0.6984 | 0.3313 | 0.4652 | 0.6832 | 0.8799 | 0.8838 | 1610.0 | 1610 | 1610 | 16.8143 | 100.0 |
Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
