| --- |
| pretty_name: "MultiCoNER v1" |
| license: "cc-by-4.0" |
| task_categories: ["token-classification"] |
| language: ["bn", "de", "en", "es", "fa", "hi", "ko", "nl", "ru", "tr", "zh"] |
| tags: ["named-entity-recognition", "ner", "multilingual", "multiconer", "semeval-2022"] |
| --- |
| |
| # MultiCoNER v1 |
|
|
| MultiCoNER v1 is a multilingual dataset for complex named entity recognition. |
|
|
| This repository is an unofficial convenience mirror created to make the publicly available dataset easier to access through Hugging Face. I did not create or own this dataset. All credit belongs to the original MultiCoNER authors and the organizers of SemEval-2022 Task 11. |
|
|
| ## Entity types |
|
|
| The dataset uses BIO annotations for six entity types: Person (`PER`), Location (`LOC`), Group (`GRP`), Corporation (`CORP`), Product (`PROD`), and Creative Work (`CW`). |
|
|
| ## Data format |
|
|
| The files are provided in CoNLL format. Each line contains a token and its named entity label, while empty lines separate samples. |
|
|
| ## Download |
|
|
| ```python |
| from huggingface_hub import snapshot_download |
| |
| dataset_path = snapshot_download( |
| repo_id="samanjoy2/multiconer_v1", |
| repo_type="dataset" |
| ) |
| ``` |
|
|
| ## License |
|
|
| The original dataset is distributed under the Creative Commons Attribution 4.0 International license. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @inproceedings{malmasi-etal-2022-multiconer, |
| title = "{M}ulti{C}o{NER}: A Large-scale Multilingual Dataset for Complex Named Entity Recognition", |
| author = "Malmasi, Shervin and Fang, Anjie and Fetahu, Besnik and Kar, Sudipta and Rokhlenko, Oleg", |
| booktitle = "Proceedings of the 29th International Conference on Computational Linguistics", |
| year = "2022", |
| pages = "3798--3809", |
| url = "https://aclanthology.org/2022.coling-1.334/" |
| } |
| ``` |
|
|
| This repository is only a convenience mirror. Please cite the original dataset authors. |
|
|
