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@@ -53,7 +53,7 @@ Other available models at [SapienzaNLP huggingface hub](https://huggingface.co/c
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  N.B. Each dataset has different annotation guidelines, choose your model according to your use case.
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  ### Results on OntoNotes
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- <img src="https://cdn-uploads.huggingface.co/production/uploads/65e9ccd84ce78d665a50f78b/-5Wi_xL2o-71uQcl3d8B9.png" alt="drawing" width="70%"/>
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  ## Maverick: Efficient and Accurate Coreference Resolution Defying recent trends
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@@ -75,5 +75,4 @@ N.B. Each dataset has different annotation guidelines, choose your model accordi
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  address = "Bangkok, Thailand",
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  publisher = "Association for Computational Linguistics",
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  }
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- ```
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- [F-coref: Fast, Accurate and Easy to Use Coreference Resolution](https://aclanthology.org/2022.aacl-demo.6) (Otmazgin et al., AACL-IJCNLP 2022)
 
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  N.B. Each dataset has different annotation guidelines, choose your model according to your use case.
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  ### Results on OntoNotes
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+ <img src="https://cdn-uploads.huggingface.co/production/uploads/65e9ccd84ce78d665a50f78b/-5Wi_xL2o-71uQcl3d8B9.png" alt="drawing" width="95%"/>
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  ## Maverick: Efficient and Accurate Coreference Resolution Defying recent trends
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  address = "Bangkok, Thailand",
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  publisher = "Association for Computational Linguistics",
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  }
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+ ```