
## 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)
{"language": ["en"], "license": "mit", "tags": ["fast", "coreference-resolution"], "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": [{"type": "CoNLL", "value": 78.5, "name": "Avg. F1"}]}]}]}
transformers pytorch roberta fast coreference-resolution en dataset:multi_news dataset:ontonotes arxiv:2209.04280 arxiv:2205.12644 arxiv:1907.10529 arxiv:2101.00434 arxiv:2109.04127 license:mit model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #fast #coreference-resolution #en #dataset-multi_news #dataset-ontonotes #arxiv-2209.04280 #arxiv-2205.12644 #arxiv-1907.10529 #arxiv-2101.00434 #arxiv-2109.04127 #license-mit #model-index #endpoints_compatible #region-us 

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: Joshi et al. (2020), Runtime: 12:06, Memory: 27.4
Model: Otmazgin et al. (2022), Runtime: 06:43, Memory: 4.6
Model: + Batching, Runtime: 06:00, Memory: 6.6
Model: Kirstain et al. (2021), Runtime: 04:37, Memory: 4.4
Model: Dobrovolskii (2021), Runtime: 03:49, Memory: 3.5
Model: F-Coref, Runtime: 00:45, Memory: 3.3
Model: + Batching, Runtime: 00:35, Memory: 4.5
Model: + Leftovers batching, Runtime: 00:25, Memory: 4.0
Model: The inference time(Min:Sec) and memory(GiB) for each model on 2.8K documents. Average of 3 runs. Hardware, NVIDIA Tesla V100 SXM2., Runtime: , Memory: 


F-coref: Fast, Accurate and Easy to Use Coreference Resolution (Otmazgin et al., AACL-IJCNLP 2022)



#### Experiments


Model: Joshi et al. (2020), Runtime: 12:06, Memory: 27.4
Model: Otmazgin et al. (2022), Runtime: 06:43, Memory: 4.6
Model: + Batching, Runtime: 06:00, Memory: 6.6
Model: Kirstain et al. (2021), Runtime: 04:37, Memory: 4.4
Model: Dobrovolskii (2021), Runtime: 03:49, Memory: 3.5
Model: F-Coref, Runtime: 00:45, Memory: 3.3
Model: + Batching, Runtime: 00:35, Memory: 4.5
Model: + Leftovers batching, Runtime: 00:25, Memory: 4.0
Model: The inference time(Min:Sec) and memory(GiB) for each model on 2.8K documents. Average of 3 runs. Hardware, NVIDIA Tesla V100 SXM2., Runtime: , Memory: 


F-coref: Fast, Accurate and Easy to Use Coreference Resolution (Otmazgin et al., AACL-IJCNLP 2022)
TAGS
#transformers #pytorch #roberta #fast #coreference-resolution #en #dataset-multi_news #dataset-ontonotes #arxiv-2209.04280 #arxiv-2205.12644 #arxiv-1907.10529 #arxiv-2101.00434 #arxiv-2109.04127 #license-mit #model-index #endpoints_compatible #region-us 
 #### Experiments


Model: Joshi et al. (2020), Runtime: 12:06, Memory: 27.4
Model: Otmazgin et al. (2022), Runtime: 06:43, Memory: 4.6
Model: + Batching, Runtime: 06:00, Memory: 6.6
Model: Kirstain et al. (2021), Runtime: 04:37, Memory: 4.4
Model: Dobrovolskii (2021), Runtime: 03:49, Memory: 3.5
Model: F-Coref, Runtime: 00:45, Memory: 3.3
Model: + Batching, Runtime: 00:35, Memory: 4.5
Model: + Leftovers batching, Runtime: 00:25, Memory: 4.0
Model: The inference time(Min:Sec) and memory(GiB) for each model on 2.8K documents. Average of 3 runs. Hardware, NVIDIA Tesla V100 SXM2., Runtime: , Memory: 


F-coref: Fast, Accurate and Easy to Use Coreference Resolution (Otmazgin et al., AACL-IJCNLP 2022)
passage: TAGS
#transformers #pytorch #roberta #fast #coreference-resolution #en #dataset-multi_news #dataset-ontonotes #arxiv-2209.04280 #arxiv-2205.12644 #arxiv-1907.10529 #arxiv-2101.00434 #arxiv-2109.04127 #license-mit #model-index #endpoints_compatible #region-us 
#### Experiments


Model: Joshi et al. (2020), Runtime: 12:06, Memory: 27.4
Model: Otmazgin et al. (2022), Runtime: 06:43, Memory: 4.6
Model: + Batching, Runtime: 06:00, Memory: 6.6
Model: Kirstain et al. (2021), Runtime: 04:37, Memory: 4.4
Model: Dobrovolskii (2021), Runtime: 03:49, Memory: 3.5
Model: F-Coref, Runtime: 00:45, Memory: 3.3
Model: + Batching, Runtime: 00:35, Memory: 4.5
Model: + Leftovers batching, Runtime: 00:25, Memory: 4.0
Model: The inference time(Min:Sec) and memory(GiB) for each model on 2.8K documents. Average of 3 runs. Hardware, NVIDIA Tesla V100 SXM2., Runtime: , Memory: 


F-coref: Fast, Accurate and Easy to Use Coreference Resolution (Otmazgin et al., AACL-IJCNLP 2022)