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--- |
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license: mit |
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base_model: facebook/bart-large-cnn |
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tags: |
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- generated_from_trainer |
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metrics: |
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- rouge |
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model-index: |
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- name: 01_ToS-BART |
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results: [] |
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datasets: |
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- EE21/ToS-Summaries |
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language: |
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- en |
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pipeline_tag: summarization |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# BART-ToSSimplify |
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This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3895 |
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- Rouge1: 0.6186 |
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- Rouge2: 0.4739 |
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- Rougel: 0.5159 |
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- Rougelsum: 0.5152 |
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- Gen Len: 108.6354 |
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## Model description |
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BART-ToSSimplify is designed to generate summaries of Terms of Service documents. |
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## Intended uses & limitations |
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Intended Uses: |
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- Generating simplified summaries of Terms of Service agreements. |
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- Automating the summarization of legal documents for quick comprehension. |
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Limitations: |
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- This model is specifically designed for the English language and cannot be applied to other languages. |
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- The quality of generated summaries may vary based on the complexity of the source text. |
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## Training and evaluation data |
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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. |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 5 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:--------:| |
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| No log | 1.0 | 360 | 0.3310 | 0.5585 | 0.4013 | 0.4522 | 0.4522 | 116.1105 | |
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| 0.2783 | 2.0 | 720 | 0.3606 | 0.5719 | 0.4078 | 0.4572 | 0.4568 | 114.6796 | |
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| 0.2843 | 3.0 | 1080 | 0.3829 | 0.6019 | 0.4456 | 0.4872 | 0.4875 | 110.8066 | |
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| 0.2843 | 4.0 | 1440 | 0.3599 | 0.6092 | 0.4604 | 0.5049 | 0.5049 | 110.884 | |
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| 0.1491 | 5.0 | 1800 | 0.3895 | 0.6186 | 0.4739 | 0.5159 | 0.5152 | 108.6354 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
