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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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+
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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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+
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+ ## Model Details
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+
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+ ### Model Description
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+
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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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+
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+ ## Uses
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+
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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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+
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+ ### Recommendations
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+
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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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+
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+
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+ ### Training Data
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+
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+ This model was pretrained on the [Multi Legal Neg Dataset](https://huggingface.co/datasets/rcds/MultiLegalNeg)
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+
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+ ## Evaluation
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+
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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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+
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+ ```
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+ #### Software
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+
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+ pytorch, transformers.
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+
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+ ## Citation
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+
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+ ```
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+ TBD
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+ ```