metadata

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

BART-ToSSimplify

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

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