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--- |
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datasets: |
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- rcds/MultiLegalNeg |
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language: |
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- de |
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- fr |
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- it |
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- en |
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tags: |
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- legal |
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--- |
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# Model Card for joelito/legal-swiss-longformer-base |
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This model is based on [XLM-R-Base](https://huggingface.co/xlm-roberta-base). |
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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)) |
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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 ([ |
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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)). |
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## Model Details |
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### Model Description |
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- **Model type:** Transformer-based language model (XLM-R-base) |
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- **Languages:** de, fr, it, en |
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- **License:** CC BY-SA |
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- **Finetune Task:** Negation Scope Resolution |
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## Uses |
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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. |
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### Recommendations |
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. |
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### Training Data |
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This model was pretrained on the [Multi Legal Neg Dataset](https://huggingface.co/datasets/rcds/MultiLegalNeg) |
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## Evaluation |
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We evaluate neg-xlm-roberta-base on the test sets in the [Multi Legal Neg Dataset](https://huggingface.co/datasets/rcds/MultiLegalNeg). |
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| \_Test Dataset | F1-score | |
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| :------------------------- | :-------- | |
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| fr | 92.49 | |
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| it | 88.81 | |
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| de (DE) | 95.66 | |
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| de (CH) | 87.82 | |
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| SFU Review | 88.53 | |
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| ConanDoyle-neg | 90.47 | |
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| BioScope | 95.59 | |
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| Dalloux | 93.99 | |
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#### Software |
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pytorch, transformers. |
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## Citation |
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Please cite the following preprint: |
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``` |
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@misc{christen2023resolving, |
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title={Resolving Legalese: A Multilingual Exploration of Negation Scope Resolution in Legal Documents}, |
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author={Ramona Christen and Anastassia Shaitarova and Matthias Stürmer and Joel Niklaus}, |
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year={2023}, |
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eprint={2309.08695}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
