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---
language:
- en
tags:
- coreference-resolution
- maverick
- efficient
- accurate
license:
- cc-by-nc-sa-4.0
datasets:
- OntoNotes
metrics:
- CoNLL
task_categories:
- coreference-resolution
model-index:
- name: sapienzanlp/maverick-mes-ontonotes
results:
- task:
type: coreference-resolution
name: coreference-resolution
dataset:
name: ontonotes
type: coreference
metrics:
- name: Avg. F1
type: CoNLL
value: 83.6
---
# Maverick mes OntoNotes
Official weights for *Maverick-mes* trained on OntoNotes and based on DeBERTa-large.
This model achieves 83.6 Avg CoNLL-F1 on OntoNotes.
Other available models at [SapienzaNLP huggingface hub](https://huggingface.co/collections/sapienzanlp/maverick-coreference-resolution-66a750a50246fad8d9c7086a):
| hf_model_name | training dataset | Score | Singletons |
|:-----------------------------------:|:----------------:|:-----:|:----------:|
| ["sapienzanlp/maverick-mes-ontonotes"](https://huggingface.co/sapienzanlp/maverick-mes-ontonotes) | OntoNotes | 83.6 | No |
| ["sapienzanlp/maverick-mes-litbank"](https://huggingface.co/sapienzanlp/maverick-mes-litbank) | LitBank | 78.0 | Yes |
| ["sapienzanlp/maverick-mes-preco"](https://huggingface.co/sapienzanlp/maverick-mes-preco) | PreCo | 87.4 | Yes |
<!-- | ["sapienzanlp/maverick-s2e-ontonotes"](https://huggingface.co/sapienzanlp/maverick-mes-preco) | OntoNotes | 83.4 | No | No | -->
<!-- | "sapienzanlp/maverick-incr-ontonotes" | Ontonotes | 83.5 | No | No | -->
<!-- | "sapienzanlp/maverick-mes-ontonotes-base" | Ontonotes | 81.4 | No | No | -->
<!-- | "sapienzanlp/maverick-s2e-ontonotes-base" | Ontonotes | 81.1 | No | No | -->
<!-- | "sapienzanlp/maverick-incr-ontonotes-base" | Ontonotes | 81.0 | No | No | -->
<!-- | "sapienzanlp/maverick-s2e-litbank" | LitBank | 77.6 | Yes | No | -->
<!-- | "sapienzanlp/maverick-incr-litbank" | LitBank | 78.3 | Yes | No | -->
<!-- | "sapienzanlp/maverick-s2e-preco" | PreCo | 87.2 | Yes | No | -->
<!-- | "sapienzanlp/maverick-incr-preco" | PreCo | 88.0 | Yes | No | -->
N.B. Each dataset has different annotation guidelines, choose your model according to your use case.
### Results on OntoNotes
<img src="https://cdn-uploads.huggingface.co/production/uploads/65e9ccd84ce78d665a50f78b/-5Wi_xL2o-71uQcl3d8B9.png" alt="drawing" width="95%"/>
## Maverick: Efficient and Accurate Coreference Resolution Defying recent trends
[![Conference](https://img.shields.io/badge/ACL%202024%20Paper-red)](https://arxiv.org/pdf/2407.21489)
[![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/)
[![Pip Package](https://img.shields.io/badge/๐Ÿ%20Python%20package-blue)](https://pypi.org/project/maverick-coref/)
[![git](https://img.shields.io/badge/Git%20Repo%20-yellow.svg)](https://github.com/SapienzaNLP/maverick-coref)
### Citation
```
@inproceedings{martinelli-etal-2024-maverick,
title = "Maverick: Efficient and Accurate Coreference Resolution Defying Recent Trends",
author = "Martinelli, Giuliano and
Barba, Edoardo and
Navigli, Roberto",
booktitle = "Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL 2024)",
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
}
```