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---
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 |