f-coref / README.md
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metadata
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 5.0
          type: coreference
        metrics:
          - name: MUC
            type: precision
            value: 85

F-Coref: Fast, Accurate and Easy to Use Coreference Resolution

F-Coref allows to process 2.8K OntoNotes documents in 25 seconds on a V100 GPU (compared to 6 minutes for the LingMess 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 for more details and updates.

Experiments

Model Runtime Memory
Joshi et al. (2020) 12:06 27.4
Otmazgin et al. (2022) 06:43 4.6
+ Batching 06:00 6.6
Kirstain et al. (2021) 04:37 4.4
Dobrovolskii (2021) 03:49 3.5
F-Coref 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.

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