f-coref

biu-nlp/f-coref

f-coref is a machine learning model from biu-nlp. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as mit.

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At a glance

Library: transformers
License: mit
Model type: roberta
Access: Public
Created: Aug 19, 2022
Updated: Nov 28, 2022
SHA: e91dfff1

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From the Hugging Face model README

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.

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 (Otmazgin et al., AACL-IJCNLP 2022)

Library: transformers
Type: roberta
License: mit
Languages: en
Created: Aug 19, 2022
Updated: Nov 28, 2022