--- 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} } ```