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--- |
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language: |
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- en |
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tags: |
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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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- ontonotes |
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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-ontonotes |
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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: ontonotes |
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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: 83.6 |
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--- |
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# Maverick mes OntoNotes |
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Official Maverick-mes trained on OntoNotes and based on DeBERTa-large. |
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This model achieves 83.6 CoNLLF1 on OntoNotes. |
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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" | 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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### 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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- [![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/๐%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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[F-coref: Fast, Accurate and Easy to Use Coreference Resolution](https://aclanthology.org/2022.aacl-demo.6) (Otmazgin et al., AACL-IJCNLP 2022) |