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README.md

@@ -480,3 +480,46 @@ dataset_info:
   dataset_size: 35559142
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+# Multilingual Complex Named Entity Recognition (MultiCoNER) 
+
+## Dataset Summary
+MultiCoNER is a large multilingual dataset for Named Entity Recognition that covers 3 domains (Wiki sentences, questions, and search queries) across 11 languages, as well as multilingual and code-mixing subsets. This dataset is designed to represent contemporary challenges in NER, including low-context scenarios (short and uncased text), syntactically complex entities like movie titles, and long-tail entity distributions. The 26M token dataset is compiled from public resources using techniques such as heuristic-based sentence sampling, template extraction and slotting, and machine translation. 
+
+See the [AWS Open Data Registry entry for MultiCoNER](https://registry.opendata.aws/multiconer/) for more information.
+
+## Labels
+* `PER`: Person, i.e. names of people
+* `LOC`: Location, i.e. locations/physical facilities
+* `CORP`: Corporation, i.e. corporations/businesses
+* `GRP`: Groups, i.e. all other groups
+* `PROD`: Product, i.e. consumer products
+* `CW`: Creative Work, i.e. movies/songs/book titles
+
+## Languages
+The MultiCoNER dataset consists of the following languages: Bangla, German, English, Spanish, Farsi, Hindi, Korean, Dutch, Russian, Turkish and Chinese.
+
+## Dataset Structure
+The dataset follows the IOB format of CoNLL.
+
+## Usage
+```python
+from datasets import load_dataset
+
+dataset = load_dataset('tomaarsen/MultiCoNER', 'multi')
+```
+
+## License
+
+CC BY 4.0
+
+## Citation
+```
+@misc{malmasi2022multiconer,
+    title={MultiCoNER: A Large-scale Multilingual dataset for Complex Named Entity Recognition}, 
+    author={Shervin Malmasi and Anjie Fang and Besnik Fetahu and Sudipta Kar and Oleg Rokhlenko},
+    year={2022},
+    eprint={2208.14536},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```