maverick-mes-preco

Maverick mes PreCo

Official weights for Maverick-mes trained on PreCo and based on DeBERTa-large. This model achieves 87.4 Avg CoNLL-F1 on PreCo coreference resolution dataset.

Other available models at SapienzaNLP huggingface hub:

<!-- | ["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.

Maverick: Efficient and Accurate Coreference Resolution Defying recent trends

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",
}