---
title: chemdner
canonical_url: "https://www.modelscope.cn/datasets/jablonkagroup/chemdner"
md_url: "https://www.modelscope.cn/datasets/jablonkagroup/chemdner.md"
repository: jablonkagroup/chemdner
last_updated: 2025-05-27
license: "Apache License 2.0"
storage_size: "89 MB"
downloads: 830
stars: 0
---

# chemdner

> chemdner - jablonkagroup 在 ModelScope 开源的数据集。The CHEMDNER corpus comprises 10,000 PubMed abstracts, which have been meticulously annotated by expert chemistry literature curators according to task-specific guidelines, identifying a total of 84,355 mentions…

jablonkagroup/chemdner 是 ModelScope 魔搭社区上的数据集，存储大小 89 MB，采用 Apache License 2.0 许可。

- **Repository**: jablonkagroup/chemdner
- **License**: Apache License 2.0
- **Storage size**: 89 MB
- **Downloads**: 830
- **Stars**: 0
- **Last updated**: 2025-05-27

Source: https://www.modelscope.cn/datasets/jablonkagroup/chemdner

---

## Dataset Details

### Dataset Description

The CHEMDNER corpus comprises 10,000 PubMed abstracts, which have been meticulously annotated by expert chemistry literature curators according to task-specific guidelines, identifying a total of 84,355 mentions of chemical entities.    The CHEMDNER corpus is a collection of 10,000 PubMed abstracts that contain a total of 84,355 chemical entity mentions labeled manually by expert chemistry literature curators, following annotation guidelines specifically defined for this task.

- **Curated by:**
- **License:** unknown

### Dataset Sources

- [original dataset](https://huggingface.co/datasets/bigbio/chemdner)

## Citation

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

```bibtex
@article{Krallinger2015,
title        = {The CHEMDNER corpus of chemicals and drugs and its annotation principles},
author       = {
  Krallinger, Martin and Rabal, Obdulia and Leitner, Florian and Vazquez,
  Miguel and Salgado, David and Lu, Zhiyong and Leaman, Robert and Lu, Yanan
  and Ji, Donghong and Lowe, Daniel M. and Sayle, Roger A. and
  Batista-Navarro, Riza Theresa and Rak, Rafal and Huber, Torsten and
  Rockt{"a}schel, Tim and Matos, S{'e}rgio and Campos, David and Tang,
  Buzhou and Xu, Hua and Munkhdalai, Tsendsuren and Ryu, Keun Ho and Ramanan,
  S. V. and Nathan, Senthil and {{Z}}itnik, Slavko and Bajec, Marko and
  Weber, Lutz and Irmer, Matthias and Akhondi, Saber A. and Kors, Jan A. and
  Xu, Shuo and An, Xin and Sikdar, Utpal Kumar and Ekbal, Asif and Yoshioka,
  Masaharu and Dieb, Thaer M. and Choi, Miji and Verspoor, Karin and Khabsa,
  Madian and Giles, C. Lee and Liu, Hongfang and Ravikumar, Komandur
  Elayavilli and Lamurias, Andre and Couto, Francisco M. and Dai, Hong-Jie
  and Tsai, Richard Tzong-Han and Ata, Caglar and Can, Tolga and Usi{'e},
  Anabel and Alves, Rui and Segura-Bedmar, Isabel and Mart{'i}nez, Paloma
  and Oyarzabal, Julen and Valencia, Alfonso
},
year         = 2015,
month        = {Jan},
day          = 19,
journal      = {Journal of Cheminformatics},
volume       = 7,
number       = 1,
pages        = {S2},
doi          = {10.1186/1758-2946-7-S1-S2},
issn         = {1758-2946},
url          = {https://doi.org/10.1186/1758-2946-7-S1-S2},
abstract     = {
  The automatic extraction of chemical information from text requires the
  recognition of chemical entity mentions as one of its key steps. When
  developing supervised named entity recognition (NER) systems, the
  availability of a large, manually annotated text corpus is desirable.
  Furthermore, large corpora permit the robust evaluation and comparison of
  different approaches that detect chemicals in documents. We present the
  CHEMDNER corpus, a collection of 10,000 PubMed abstracts that contain a
  total of 84,355 chemical entity mentions labeled manually by expert
  chemistry literature curators, following annotation guidelines specifically
  defined for this task. The abstracts of the CHEMDNER corpus were selected
  to be representative for all major chemical disciplines. Each of the
  chemical entity mentions was manually labeled according to its
  structure-associated chemical entity mention (SACEM) class: abbreviation,
  family, formula, identifier, multiple, systematic and trivial. The
  difficulty and consistency of tagging chemicals in text was measured using
  an agreement study between annotators, obtaining a percentage agreement of
  91. For a subset of the CHEMDNER corpus (the test set of 3,000 abstracts)
  we provide not only the Gold Standard manual annotations, but also mentions
  automatically detected by the 26 teams that participated in the BioCreative
  IV CHEMDNER chemical mention recognition task. In addition, we release the
  CHEMDNER silver standard corpus of automatically extracted mentions from
  17,000 randomly selected PubMed abstracts. A version of the CHEMDNER corpus
  in the BioC format has been generated as well. We propose a standard for
  required minimum information about entity annotations for the construction
  of domain specific corpora on chemical and drug entities. The CHEMDNER
  corpus and annotation guidelines are available at:
  ttp://www.biocreative.org/resources/biocreative-iv/chemdner-corpus/
}
}
```