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text_shortening_model_v64
This model is a fine-tuned version of t5-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.3622
- Bert precision: 0.7381
- Bert recall: 0.7763
- Bert f1-score: 0.7541
- Average word count: 9.0345
- Max word count: 14
- Min word count: 2
- Average token count: 15.5862
- % shortened texts with length > 12: 20.6897
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: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Bert precision | Bert recall | Bert f1-score | Average word count | Max word count | Min word count | Average token count | % shortened texts with length > 12 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 3.1461 | 1.0 | 5 | 2.3622 | 0.7381 | 0.7763 | 0.7541 | 9.0345 | 14 | 2 | 15.5862 | 20.6897 |
Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
