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README.md

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+---
+language:
+- en
+license: mit
+tags:
+- GECToR
+- Grammar Error Correction
+- xlnet-base-cased
+pipeline_tag: token-classification
+---
+
+### License
+The model is licensed under the MIT License
+### Sample Code
+
+### Citation
+
+You can find the original code at https://github.com/grammarly/gector
+
+You can find the paper at https://aclanthology.org/2020.bea-1.16/
+
+```bib
+@inproceedings{omelianchuk-etal-2020-gector,
+    title = "{GECT}o{R} {--} Grammatical Error Correction: Tag, Not Rewrite",
+    author = "Omelianchuk, Kostiantyn  and
+      Atrasevych, Vitaliy  and
+      Chernodub, Artem  and
+      Skurzhanskyi, Oleksandr",
+    booktitle = "Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications",
+    month = jul,
+    year = "2020",
+    address = "Seattle, WA, USA → Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/2020.bea-1.16",
+    pages = "163--170",
+    abstract = "In this paper, we present a simple and efficient GEC sequence tagger using a Transformer encoder. Our system is pre-trained on synthetic data and then fine-tuned in two stages: first on errorful corpora, and second on a combination of errorful and error-free parallel corpora. We design custom token-level transformations to map input tokens to target corrections. Our best single-model/ensemble GEC tagger achieves an F{\_}0.5 of 65.3/66.5 on CONLL-2014 (test) and F{\_}0.5 of 72.4/73.6 on BEA-2019 (test). Its inference speed is up to 10 times as fast as a Transformer-based seq2seq GEC system.",
+}
+```