README.md

metadata

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 Maverick-mes trained on OntoNotes and based on DeBERTa-large. This model achieves 83.6 CoNLLF1 on OntoNotes.

Other available models at SapienzaNLP huggingface hub:

hf_model_name	training dataset	Score	Singletons
"sapienzanlp/maverick-mes-ontonotes"	OntoNotes	83.6	No
"sapienzanlp/maverick-mes-litbank"	LitBank	78.0	Yes
"sapienzanlp/maverick-mes-preco"	PreCo	87.4	Yes

N.B. Each dataset has different annotation guidelines, choose your model according to your use case.

Results on OntoNotes

Maverick: Efficient and Accurate Coreference Resolution Defying recent trends

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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",
}

F-coref: Fast, Accurate and Easy to Use Coreference Resolution (Otmazgin et al., AACL-IJCNLP 2022)