đ BART-ToSSimplify
BART-ToSSimplify is a fine - tuned model based on facebook/bart-large-cnn. It is designed to generate summaries of Terms of Service documents, which can simplify the comprehension of legal texts.
đ Quick Start
This model is a fine - tuned version of facebook/bart-large-cnn on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3895
- Rouge1: 0.6186
- Rouge2: 0.4739
- Rougel: 0.5159
- Rougelsum: 0.5152
- Gen Len: 108.6354
⨠Features
Model description
BART-ToSSimplify is designed to generate summaries of Terms of Service documents.
Intended uses & limitations
Intended Uses
- Generating simplified summaries of Terms of Service agreements.
- Automating the summarization of legal documents for quick comprehension.
Limitations
- This model is specifically designed for the English language and cannot be applied to other languages.
- The quality of generated summaries may vary based on the complexity of the source text.
đĻ Installation
No installation steps are provided in the original document, so this section is skipped.
đģ Usage Examples
No code examples are provided in the original document, so this section is skipped.
đ Documentation
Training and evaluation data
BART-ToSSimplify was trained on a dataset consisting of summaries of various Terms of Service agreements. The dataset was collected and preprocessed to create a training and evaluation split.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e - 05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss |
Epoch |
Step |
Validation Loss |
Rouge1 |
Rouge2 |
Rougel |
Rougelsum |
Gen Len |
| No log |
1.0 |
360 |
0.3310 |
0.5585 |
0.4013 |
0.4522 |
0.4522 |
116.1105 |
| 0.2783 |
2.0 |
720 |
0.3606 |
0.5719 |
0.4078 |
0.4572 |
0.4568 |
114.6796 |
| 0.2843 |
3.0 |
1080 |
0.3829 |
0.6019 |
0.4456 |
0.4872 |
0.4875 |
110.8066 |
| 0.2843 |
4.0 |
1440 |
0.3599 |
0.6092 |
0.4604 |
0.5049 |
0.5049 |
110.884 |
| 0.1491 |
5.0 |
1800 |
0.3895 |
0.6186 |
0.4739 |
0.5159 |
0.5152 |
108.6354 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
đ§ Technical Details
No specific technical implementation details (more than 50 words) are provided in the original document, so this section is skipped.
đ License
This model is released under the MIT license.
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