| --- |
| license: mit |
| base_model: facebook/bart-large-cnn |
| tags: |
| - generated_from_trainer |
| metrics: |
| - rouge |
| model-index: |
| - name: 01_ToS-BART |
| results: [] |
| datasets: |
| - EE21/ToS-Summaries |
| language: |
| - en |
| pipeline_tag: summarization |
| --- |
| |
| <!-- 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. --> |
|
|
| # BART-ToSSimplify |
|
|
| This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/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 |
|
|
| ## 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. |
|
|
| ## 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 |
