| ## 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 |