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---
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) |