Create README.md
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README.md
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---
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language:
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- en
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tags:
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- efficiency
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- coreference-resolution
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- maverick
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- efficient
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- accurate
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license:
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- cc-by-nc-sa-4.0
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datasets:
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- LitBank
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metrics:
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- CoNLL
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task_categories:
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- coreference-resolution
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model-index:
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- name: sapienzanlp/maverick-mes-litbank
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results:
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- task:
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type: coreference-resolution
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name: coreference-resolution
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dataset:
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name: litbank
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type: coreference
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metrics:
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- name: Avg. F1
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type: CoNLL
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value: 78.0
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---
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# Maverick mes LitBank
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Official weights for *Maverick-mes* trained on LitBank and based on DeBERTa-large.
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This model achieves 78.0 Avg CoNLL-F1 on LitBank.
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Other available models at [SapienzaNLP huggingface hub](https://huggingface.co/collections/sapienzanlp/maverick-coreference-resolution-66a750a50246fad8d9c7086a):
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| hf_model_name | training dataset | Score | Singletons |
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|:-----------------------------------:|:----------------:|:-----:|:----------:|
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| ["sapienzanlp/maverick-mes-ontonotes"](https://huggingface.co/sapienzanlp/maverick-mes-ontonotes) | OntoNotes | 83.6 | No |
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| ["sapienzanlp/maverick-mes-litbank"](https://huggingface.co/sapienzanlp/maverick-mes-litbank) | LitBank | 78.0 | Yes |
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| ["sapienzanlp/maverick-mes-preco"](https://huggingface.co/sapienzanlp/maverick-mes-preco) | PreCo | 87.4 | Yes |
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<!-- | ["sapienzanlp/maverick-s2e-ontonotes"](https://huggingface.co/sapienzanlp/maverick-mes-preco) | OntoNotes | 83.4 | No | No | -->
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<!-- | "sapienzanlp/maverick-incr-ontonotes" | Ontonotes | 83.5 | No | No | -->
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<!-- | "sapienzanlp/maverick-mes-ontonotes-base" | Ontonotes | 81.4 | No | No | -->
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<!-- | "sapienzanlp/maverick-s2e-ontonotes-base" | Ontonotes | 81.1 | No | No | -->
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<!-- | "sapienzanlp/maverick-incr-ontonotes-base" | Ontonotes | 81.0 | No | No | -->
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<!-- | "sapienzanlp/maverick-s2e-litbank" | LitBank | 77.6 | Yes | No | -->
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<!-- | "sapienzanlp/maverick-incr-litbank" | LitBank | 78.3 | Yes | No | -->
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<!-- | "sapienzanlp/maverick-s2e-preco" | PreCo | 87.2 | Yes | No | -->
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<!-- | "sapienzanlp/maverick-incr-preco" | PreCo | 88.0 | Yes | No | -->
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N.B. Each dataset has different annotation guidelines, choose your model according to your use case.
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## Maverick: Efficient and Accurate Coreference Resolution Defying recent trends
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[![Conference](https://img.shields.io/badge/ACL%202024%20Paper-red)](https://arxiv.org/pdf/2407.21489)
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[![License: CC BY-NC 4.0](https://img.shields.io/badge/License-CC%20BY--NC%204.0-green.svg)](https://creativecommons.org/licenses/by-nc/4.0/)
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[![Pip Package](https://img.shields.io/badge/🐍%20Python%20package-blue)](https://pypi.org/project/maverick-coref/)
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[![git](https://img.shields.io/badge/Git%20Repo%20-yellow.svg)](https://github.com/SapienzaNLP/maverick-coref)
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### Citation
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```
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@inproceedings{martinelli-etal-2024-maverick,
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title = "Maverick: Efficient and Accurate Coreference Resolution Defying Recent Trends",
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author = "Martinelli, Giuliano and
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Barba, Edoardo and
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Navigli, Roberto",
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booktitle = "Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL 2024)",
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year = "2024",
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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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