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README.md

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+{}
+---
+# Dataset Card for OpenNER 1.0
+
+<!-- Provide a quick summary of the dataset. -->
+
+OpenNER 1.0 is a standardized collection of openly-available named entity recognition (NER) datasets.
+OpenNER contains 36 NER corpora that span 52 languages, human-annotated in varying named entity ontologies.
+We correct annotation format issues, standardize the original datasets into a uniform representation with consistent entity type names across corpora, and provide the collection in a structure that enables research in multilingual and multi-ontology NER.
+
+This version of the dataset contains all original entity types for each dataset after validation and normalization.
+
+## Dataset Details
+
+### Dataset Description
+
+<!-- Provide a longer summary of what this dataset is. -->
+
+- **Curated by:** BLT Lab: Chester Palen-Michel, Maxwell Pickering, Maya Kruse, Jonne Sälevä, & Constantine Lignos
+- **Shared by:** Chester Palen-Michel
+- **Language(s) (NLP):** Akan/Twi, Algerian Arabic,Amharic, Arabic, Bambara, Basque, Bavarian German, Catalan, Chichewa, chiShona, Croatian, Danish, Dutch, English, Éwé, Finnish, Fon, Galician, German, Ghomálá', Greek, Hausa, Hebrew, Hindi, Igbo, isiXhosa, Italian, Japanese, Kazakh, Kinyarwanda, Kiswahili, Luganda, Luo, Mandarin Chinese, Marathi, Mossi, Naija, Nepali, Norwegian, Persian Farsi, Portuguese, Romanian, Setswana, Slovak, Slovenian, Spanish, Swedish, Thai, Wolof, Yoruba, Zulu,
+- **License:** CC-BY 4.0 for the OpenNER collection. Individual datasets have their own licenses.
+
+### Dataset Sources
+
+<!-- Provide the basic links for the dataset. -->
+
+- **Repository:** [OpenNER Github](https://github.com/bltlab/open-ner)
+- **Paper:** [ACL Anthology](https://aclanthology.org/2025.emnlp-main.1708/) and [arXiv](https://arxiv.org/abs/2412.09587)
+
+## Uses
+
+<!-- Address questions around how the dataset is intended to be used. -->
+Primarily to be used for research regarding multilingual NER with different entity sets and annotation guidelines.
+
+## Dataset Structure
+
+```
+{
+  'id': '0',
+  'tokens': ['Melbourne', '(', 'Australia', ')', ',', '25', 'may', '(', 'EFE', ')', '.'],
+  'ner_tags': [5, 0, 5, 0, 0, 0, 0, 0, 3, 0, 0]
+}
+```
+
+The `ner_tags` column includes an internal `int2str` map to access the corresponding string label
+for each integer label.
+
+## Dataset Creation
+
+### Curation Rationale
+
+<!-- Motivation for the creation of this dataset. -->
+
+There are many NER datasets but many have different formats, label names, and label schemas.
+OpenNER standardizes and collects these many NER datasets in easily accessible format and place.
+
+### Source Data
+
+<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
+OpenNER primarily contains newswire and web text.
+See our paper with each cited paper describing each dataset for details of source data.
+
+#### Data Collection and Processing
+
+<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
+
+See our paper for details on data collection and processing.
+
+#### Who are the source data producers?
+
+<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
+
+See our paper with each cited paper describing each dataset for details of source data.
+
+### Annotations
+
+<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
+Annotation is named entity recognition annotation on each token in BIO format.
+
+#### Annotation process
+
+<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
+
+See our paper with each cited paper for each dataset for details on the annotation process.
+
+#### Who are the annotators?
+
+<!-- This section describes the people or systems who created the annotations. -->
+
+OpenNER is a collection of many existing datasets which have been human annotated.
+Annotator details are included in each dataset's original publication.
+Citations for each work can be found in our paper.
+
+#### Personal and Sensitive Information
+
+<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
+
+To the best of our knowledge there is no personal or sensitive information beyond that which generally occurs in newswire text.
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+See our paper for discussion of limitations and biases.
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+See our paper for recommendations regarding OpenNER.
+
+## Citation
+
+If you make use of this dataset, please cite our paper using this bibtex:
+
+**BibTeX:**
+
+```
+@inproceedings{palen-michel-etal-2025-openner,
+    title = "{O}pen{NER} 1.0: Standardized Open-Access Named Entity Recognition Datasets in 50+ Languages",
+    author = {Palen-Michel, Chester  and
+      Pickering, Maxwell  and
+      Kruse, Maya  and
+      S{\"a}lev{\"a}, Jonne  and
+      Lignos, Constantine},
+    editor = "Christodoulopoulos, Christos  and
+      Chakraborty, Tanmoy  and
+      Rose, Carolyn  and
+      Peng, Violet",
+    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
+    month = nov,
+    year = "2025",
+    address = "Suzhou, China",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2025.emnlp-main.1708/",
+    doi = "10.18653/v1/2025.emnlp-main.1708",
+    pages = "33637--33662",
+    ISBN = "979-8-89176-332-6",
+    abstract = "We present OpenNER 1.0, a standardized collection of openly-available named entity recognition (NER) datasets.OpenNER contains 36 NER corpora that span 52 languages, human-annotated in varying named entity ontologies.We correct annotation format issues, standardize the original datasets into a uniform representation with consistent entity type names across corpora, and provide the collection in a structure that enables research in multilingual and multi-ontology NER.We provide baseline results using three pretrained multilingual language models and two large language models to compare the performance of recent models and facilitate future research in NER.We find that no single model is best in all languages and that significant work remains to obtain high performance from LLMs on the NER task.OpenNER is released at https://github.com/bltlab/open-ner."
+}
+```
+
+## Dataset Card Authors
+
+Chester Palen-Michel [@cpalenmichel](https://github.com/cpalenmichel)
+
+## Dataset Card Contact
+
+Chester Palen-Michel [@cpalenmichel](https://github.com/cpalenmichel)