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@@ -42,7 +42,7 @@ We have fine-tuned all pre-trained models on 3 legal tasks with Indian datasets:
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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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  ```
@@ -55,7 +55,6 @@ This InLegalBERT beats LegalBERT as well as all other baselines/variants we have
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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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  ```
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  ### About Us
@@ -67,4 +66,4 @@ We have, and are currently working on several legal tasks such as:
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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).