Mirror README.md from lang-uk/Reddit-MultiGEC

README.md

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+---
+license: mit
+language:
+- uk
+- en
+- de
+- cs
+- it
+- et
+- sl
+- el
+- lv
+- is
+- sv
+task_categories:
+- text-generation
+pretty_name: reddit_multigec
+size_categories:
+- 100K<n<1M
+tags:
+- gec
+- multigec
+---
+
+# Reddit-MultiGEC Dataset
+
+## Overview
+Reddit-MultiGEC is a large multilingual corpus of posts scraped from Reddit, automatically corrected using the approach (TBU).
+
+## Structure
+- `reddit_multi_gec.csv` - main data.
+  - `language` - language of text; 
+  - `text` - original text; 
+  - `correction` - corrected text;
+- `reddit_uk_annotations.csv` - contains human annotations for 1500 samples for the Ukrainian language.
+  - `text` - original text; 
+  - `correction` - corrected text;
+  - `score` - annotator score;
+  - `is_rejected` - if the annotator rejects the correction.
+
+## Dataset Statistics
+|Language|Number of Examples|
+|-|-|
+|english|      49055|
+|german|       41678|
+|czech|        16621|
+|italian|      12686|
+|estonian|     10036|
+|slovene|       8427|
+|greek|         7497|
+|latvian|       6438|
+|ukrainian|     4901|
+|swedish|       1995|
+|icelandic|      196|
+
+## How to use it
+You can merge `reddit_multi_gec.csv` with `reddit_uk_annotations.csv`.
+```python
+import pandas as pd
+df_reddit = pd.read_csv('reddit_multi_gec.csv')
+df_reddit_anot = pd.read_csv('reddit_uk_annotations.csv')
+df_anot = df_reddit_anot.merge(df_reddit, on=['text', 'correction'], how='left')
+```
+
+## Citation
+If you use or discuss this project/dataset in your work, please cite our paper:\
+Paper: [Introducing OmniGEC: A Silver Multilingual Dataset for Grammatical Error Correction](https://aclanthology.org/2025.unlp-1.17.pdf)\
+Authors: [Roman Kovalchuk](https://huggingface.co/rkovalchuk), [Mariana Romanyshyn](https://huggingface.co/mariana-scorp), [Petro Ivaniuk](https://huggingface.co/peterua)\
+[Proceedings of the Fourth Ukrainian Natural Language Processing Workshop (UNLP 2025)](https://aclanthology.org/volumes/2025.unlp-1/), 2025
+
+```bibtex
+@inproceedings{kovalchuk-etal-2025-introducing,
+    title = "Introducing {O}mni{GEC}: A Silver Multilingual Dataset for Grammatical Error Correction",
+    author = "Kovalchuk, Roman  and
+      Romanyshyn, Mariana  and
+      Ivaniuk, Petro",
+    editor = "Romanyshyn, Mariana",
+    booktitle = "Proceedings of the Fourth Ukrainian Natural Language Processing Workshop (UNLP 2025)",
+    month = jul,
+    year = "2025",
+    address = "Vienna, Austria (online)",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2025.unlp-1.17/",
+    pages = "162--178",
+    ISBN = "979-8-89176-269-5",
+    abstract = "In this paper, we introduce OmniGEC, a collection of multilingual silver-standard datasets for the task of Grammatical Error Correction (GEC), covering eleven languages: Czech, English, Estonian, German, Greek, Icelandic, Italian, Latvian, Slovene, Swedish, and Ukrainian. These datasets facilitate the development of multilingual GEC solutions and help bridge the data gap in adapting English GEC solutions to multilingual GEC. The texts in the datasets originate from three sources: Wikipedia edits for the eleven target languages, subreddits from Reddit in the eleven target languages, and the Ukrainian-only UberText 2.0 social media corpus. While Wikipedia edits were derived from human-made corrections, the Reddit and UberText 2.0 data were automatically corrected with the GPT-4o-mini model. The quality of the corrections in the datasets was evaluated both automatically and manually. Finally, we fine-tune two open-source large language models {---} Aya-Expanse (8B) and Gemma-3 (12B) {---} on the multilingual OmniGEC corpora and achieve state-of-the-art (SOTA) results for paragraph-level multilingual GEC. The dataset collection and the best-performing models are available on Hugging Face."
+}
+```
+
+---
+
+*Mirror created 2026-10-11 by the JoTalbot/ukraine pipeline. Original source: https://huggingface.co/datasets/lang-uk/Reddit-MultiGEC — please credit and respect the original dataset's license.*