Create README.md

README.md

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+## 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