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README.md
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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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### Citation
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```
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year = {2022},
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copyright = {Creative Commons Attribution 4.0 International}
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}
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```
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### About Us
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* legal statute identification from facts, court judgment prediction
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* legal document matching
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You can find our publicly available codes and datasets [here](https://github.com/Law-AI)
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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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InLegalBERT beats LegalBERT as well as all other baselines/variants we have used, across all three tasks. For details, see our [paper](https://arxiv.org/abs/2209.06049).
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### Citation
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```
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year = {2022},
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copyright = {Creative Commons Attribution 4.0 International}
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}
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```
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### About Us
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* legal statute identification from facts, court judgment prediction
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* legal document matching
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You can find our publicly available codes and datasets [here](https://github.com/Law-AI).
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