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@@ -41,6 +41,7 @@ We have fine-tuned all pre-trained models on 3 legal tasks with Indian datasets:
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  * Legal Statute Identification ([ILSI Dataset](https://arxiv.org/abs/2112.14731))[Multi-label Text Classification]: Identifying relevant statutes (law articles) based on the facts of a court case
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  * Semantic Segmentation ([ISS Dataset](https://arxiv.org/abs/1911.05405))[Sentence Tagging]: Segmenting the document into 7 functional parts (semantic segments) such as Facts, Arguments, etc.
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  * Court Judgment Prediction ([ILDC Dataset](https://arxiv.org/abs/2105.13562))[Binary Text Classification]: Predicting whether the claims/petitions of a court case will be accepted/rejected
 
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  This InLegalBERT beats LegalBERT as well as all other baselines/variants we have used. For details, see our [paper](https://arxiv.org/abs/2209.06049).
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  ### Citation
 
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  * Legal Statute Identification ([ILSI Dataset](https://arxiv.org/abs/2112.14731))[Multi-label Text Classification]: Identifying relevant statutes (law articles) based on the facts of a court case
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  * Semantic Segmentation ([ISS Dataset](https://arxiv.org/abs/1911.05405))[Sentence Tagging]: Segmenting the document into 7 functional parts (semantic segments) such as Facts, Arguments, etc.
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  * Court Judgment Prediction ([ILDC Dataset](https://arxiv.org/abs/2105.13562))[Binary Text Classification]: Predicting whether the claims/petitions of a court case will be accepted/rejected
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  This InLegalBERT beats LegalBERT as well as all other baselines/variants we have used. For details, see our [paper](https://arxiv.org/abs/2209.06049).
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  ### Citation