Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Tags:
License:
| language: | |
| - en | |
| license: | |
| - other | |
| multilinguality: | |
| - monolingual | |
| size_categories: | |
| - 10K<n<100K | |
| task_categories: | |
| - token-classification | |
| task_ids: | |
| - named-entity-recognition | |
| pretty_name: CoNLL-2003 | |
| # Dataset Card for "tner/conll2003" | |
| ## Dataset Description | |
| - **Repository:** [T-NER](https://github.com/asahi417/tner) | |
| - **Paper:** [https://www.aclweb.org/anthology/W03-0419/](https://www.aclweb.org/anthology/W03-0419/) | |
| - **Dataset:** CoNLL 2003 | |
| - **Domain:** News | |
| - **Number of Entity:** 3 | |
| ### Dataset Summary | |
| CoNLL-2003 NER dataset formatted in a part of [TNER](https://github.com/asahi417/tner) project. | |
| - Entity Types: `ORG`, `PER`, `LOC`, `MISC` | |
| ## Dataset Structure | |
| ### Data Instances | |
| An example of `train` looks as follows. | |
| ``` | |
| { | |
| 'tags': ['SOCCER','-', 'JAPAN', 'GET', 'LUCKY', 'WIN', ',', 'CHINA', 'IN', 'SURPRISE', 'DEFEAT', '.'], | |
| 'tokens': [0, 0, 5, 0, 0, 0, 0, 3, 0, 0, 0, 0] | |
| } | |
| ``` | |
| ### Label ID | |
| The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/conll2003/raw/main/dataset/label.json). | |
| ```python | |
| { | |
| "O": 0, | |
| "B-ORG": 1, | |
| "B-MISC": 2, | |
| "B-PER": 3, | |
| "I-PER": 4, | |
| "B-LOC": 5, | |
| "I-ORG": 6, | |
| "I-MISC": 7, | |
| "I-LOC": 8 | |
| } | |
| ``` | |
| ### Data Splits | |
| | name |train|validation|test| | |
| |---------|----:|---------:|---:| | |
| |conll2003|14041| 3250|3453| | |
| ### Licensing Information | |
| From the [CoNLL2003 shared task](https://www.clips.uantwerpen.be/conll2003/ner/) page: | |
| > The English data is a collection of news wire articles from the Reuters Corpus. The annotation has been done by people of the University of Antwerp. Because of copyright reasons we only make available the annotations. In order to build the complete data sets you will need access to the Reuters Corpus. It can be obtained for research purposes without any charge from NIST. | |
| The copyrights are defined below, from the [Reuters Corpus page](https://trec.nist.gov/data/reuters/reuters.html): | |
| > The stories in the Reuters Corpus are under the copyright of Reuters Ltd and/or Thomson Reuters, and their use is governed by the following agreements: | |
| > | |
| > [Organizational agreement](https://trec.nist.gov/data/reuters/org_appl_reuters_v4.html) | |
| > | |
| > This agreement must be signed by the person responsible for the data at your organization, and sent to NIST. | |
| > | |
| > [Individual agreement](https://trec.nist.gov/data/reuters/ind_appl_reuters_v4.html) | |
| > | |
| > This agreement must be signed by all researchers using the Reuters Corpus at your organization, and kept on file at your organization. | |
| ### Citation Information | |
| ``` | |
| @inproceedings{tjong-kim-sang-de-meulder-2003-introduction, | |
| title = "Introduction to the {C}o{NLL}-2003 Shared Task: Language-Independent Named Entity Recognition", | |
| author = "Tjong Kim Sang, Erik F. and De Meulder, Fien", | |
| booktitle = "Proceedings of the Seventh Conference on Natural Language Learning at {HLT}-{NAACL} 2003", | |
| year = "2003", | |
| url = "https://www.aclweb.org/anthology/W03-0419", | |
| pages = "142--147", | |
| } | |
| ``` |