|
--- |
|
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 |
