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---
datasets:
- rcds/MultiLegalNeg
language:
- de
- fr
- it
- en
tags:
- legal
---
# Model Card for joelito/legal-swiss-longformer-base
This model is based on [XLM-R-Base](https://huggingface.co/xlm-roberta-base).
It was pretrained on negation scope resolution using [NegBERT](https://github.com/adityak6798/Transformers-For-Negation-and-Speculation/blob/master/Transformers_for_Negation_and_Speculation.ipynb) ([Khandelwal and Sawant 2020](https://arxiv.org/abs/1911.04211))
For training we used the [Multi Legal Neg Dataset](https://huggingface.co/datasets/rcds/MultiLegalNeg), a multilingual dataset of legal data annotated for negation cues and scopes, ConanDoyle-neg ([
Morante and Blanco. 2012](https://aclanthology.org/S12-1035/)), SFU Review ([Konstantinova et al. 2012](http://www.lrec-conf.org/proceedings/lrec2012/pdf/533_Paper.pdf)), BioScope ([Szarvas et al. 2008](https://aclanthology.org/W08-0606/)) and Dalloux ([Dalloux et al. 2020](https://clementdalloux.fr/?page_id=28)).
## Model Details
### Model Description
- **Model type:** Transformer-based language model (XLM-R-base)
- **Languages:** de, fr, it, en
- **License:** CC BY-SA
- **Finetune Task:** Negation Scope Resolution
## Uses
See [LegalNegBERT](https://github.com/RamonaChristen/Multilingual_Negation_Scope_Resolution_on_Legal_Data/blob/main/LegalNegBERT) for details on the training process and how to use this model.
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.
### Training Data
This model was pretrained on the [Multi Legal Neg Dataset](https://huggingface.co/datasets/rcds/MultiLegalNeg)
## Evaluation
We evaluate neg-xlm-roberta-base on the test sets in the [Multi Legal Neg Dataset](https://huggingface.co/datasets/rcds/MultiLegalNeg).
| \_Test Dataset | F1-score |
| :------------------------- | :-------- |
| fr | 92.49 |
| it | 88.81 |
| de (DE) | 95.66 |
| de (CH) | 87.82 |
| SFU Review | 88.53 |
| ConanDoyle-neg | 90.47 |
| BioScope | 95.59 |
| Dalloux | 93.99 |
#### Software
pytorch, transformers.
## Citation
Please cite the following preprint:
```
@misc{christen2023resolving,
title={Resolving Legalese: A Multilingual Exploration of Negation Scope Resolution in Legal Documents},
author={Ramona Christen and Anastassia Shaitarova and Matthias Stürmer and Joel Niklaus},
year={2023},
eprint={2309.08695},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |