readme: ---
  
  language: 
    - en
  tags:
  - fast
  - coreference-resolution
  license: mit
  datasets:
  - multi_news
  - ontonotes
  metrics:
  - CoNLL
  task_categories:
  - coreference-resolution
  model-index:
  - name: biu-nlp/f-coref
    results:
    - task:
        type: coreference-resolution
        name: coreference-resolution
      dataset:
        name: ontonotes
        type: coreference
      metrics:
      - name: Avg. F1
        type: CoNLL
        value: 78.5
  
  ---
  
  ## F-Coref: Fast, Accurate and Easy to Use Coreference Resolution
  
  [F-Coref](https://arxiv.org/abs/2209.04280) allows to process 2.8K OntoNotes documents in 25 seconds on a V100 GPU (compared to 6 minutes for the [LingMess](https://arxiv.org/abs/2205.12644) model, and to 12 minutes of the popular AllenNLP coreference model) with only a modest drop in accuracy.
  The fast speed is achieved through a combination of distillation of a compact model from the LingMess model, and an efficient batching implementation using a technique we call leftover
  
  Please check the [official repository](https://github.com/shon-otmazgin/fastcoref) for more details and updates.
  
  #### Experiments
  
  | Model                 | Runtime | Memory  |
  |-----------------------|---------|---------|
  | [Joshi et al. (2020)](https://arxiv.org/abs/1907.10529)    | 12:06 | 27.4 |
  | [Otmazgin et al. (2022)](https://arxiv.org/abs/2205.12644) | 06:43 | 4.6 |
  |      + Batching                                            | 06:00 | 6.6 |
  | [Kirstain et al. (2021)](https://arxiv.org/abs/2101.00434) | 04:37 | 4.4 |
  | [Dobrovolskii (2021)](https://arxiv.org/abs/2109.04127)    | 03:49 | 3.5 |
  | [F-Coref](https://arxiv.org/abs/2209.04280)                | 00:45 | 3.3 |
  |      + Batching                                            | 00:35 | 4.5 |
  |           + Leftovers batching                             | 00:25 | 4.0 |
  The inference time(Min:Sec) and memory(GiB) for each model on 2.8K documents. Average of 3 runs. Hardware, NVIDIA Tesla V100 SXM2.
  
  ### Citation
  
  ```
  @inproceedings{Otmazgin2022FcorefFA,
    title={F-coref: Fast, Accurate and Easy to Use Coreference Resolution},
    author={Shon Otmazgin and Arie Cattan and Yoav Goldberg},
    booktitle={AACL},
    year={2022}
  }
  ```
  [F-coref: Fast, Accurate and Easy to Use Coreference Resolution](https://aclanthology.org/2022.aacl-demo.6) (Otmazgin et al., AACL-IJCNLP 2022)