---
license: cc-by-4.0
tags:
- named-entity-recognition
language:
- ind
---

# indolem_ner_ugm

NER UGM is a Named Entity Recognition dataset that comprises 2,343 sentences from news articles, and was constructed at the University of Gajah Mada based on five named entity classes: person, organization, location, time, and quantity.

## Dataset Usage

Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`.

## Citation

```
@inproceedings{koto-etal-2020-indolem,
    title = "{I}ndo{LEM} and {I}ndo{BERT}: A Benchmark Dataset and Pre-trained Language Model for {I}ndonesian {NLP}",
    author = "Koto, Fajri  and
      Rahimi, Afshin  and
      Lau, Jey Han  and
      Baldwin, Timothy",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://aclanthology.org/2020.coling-main.66",
    doi = "10.18653/v1/2020.coling-main.66",
    pages = "757--770"
}
@phdthesis{fachri2014pengenalan,
  title     = {Pengenalan Entitas Bernama Pada Teks Bahasa Indonesia Menggunakan Hidden Markov Model},
  author    = {FACHRI, MUHAMMAD},
  year      = {2014},
  school    = {Universitas Gadjah Mada}
}
```

## License

Creative Commons Attribution 4.0

## Homepage

[https://indolem.github.io/](https://indolem.github.io/)

### NusaCatalogue

For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue)