
# Dataset Card for "tner/multinerd"

## Dataset Description

- **Repository:** [T-NER](https://github.com/asahi417/tner)
- **Paper:** [https://aclanthology.org/2022.findings-naacl.60/](https://aclanthology.org/2022.findings-naacl.60/)
- **Dataset:** MultiNERD
- **Domain:** Wikipedia, WikiNews
- **Number of Entity:** 18


### Dataset Summary
MultiNERD NER benchmark dataset formatted in a part of [TNER](https://github.com/asahi417/tner) project.
- Entity Types: `PER`, `LOC`, `ORG`, `ANIM`, `BIO`, `CEL`, `DIS`, `EVE`, `FOOD`, `INST`, `MEDIA`, `PLANT`, `MYTH`, `TIME`, `VEHI`, `MISC`, `SUPER`, `PHY`

## Dataset Structure

### Data Instances
An example of `train` of `de` looks as follows.

```
{
    'tokens': [ "Die", "Blätter", "des", "Huflattichs", "sind", "leicht", "mit", "den", "sehr", "ähnlichen", "Blättern", "der", "Weißen", "Pestwurz", "(", "\"", "Petasites", "albus", "\"", ")", "zu", "verwechseln", "." ],
    'tags': [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0 ]
}
```

### Label ID
The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/multinerd/raw/main/dataset/label.json).
```python
{
"O": 0,
"B-PER": 1,
"I-PER": 2,
"B-LOC": 3,
"I-LOC": 4,
"B-ORG": 5,
"I-ORG": 6,
"B-ANIM": 7,
"I-ANIM": 8,
"B-BIO": 9,
"I-BIO": 10,
"B-CEL": 11,
"I-CEL": 12,
"B-DIS": 13,
"I-DIS": 14,
"B-EVE": 15,
"I-EVE": 16,
"B-FOOD": 17,
"I-FOOD": 18,
"B-INST": 19,
"I-INST": 20,
"B-MEDIA": 21,
"I-MEDIA": 22,
"B-PLANT": 23,
"I-PLANT": 24,
"B-MYTH": 25,
"I-MYTH": 26,
"B-TIME": 27,
"I-TIME": 28,
"B-VEHI": 29,
"I-VEHI": 30,
"B-SUPER": 31,
"I-SUPER": 32,
"B-PHY": 33,
"I-PHY": 34
}
```

### Data Splits

| language   |   test |
|:-----------|-------:|
| de         | 156792 |
| en         | 164144 |
| es         | 173189 |
| fr         | 176185 |
| it         | 181927 |
| nl         | 171711 |
| pl         | 194965 |
| pt         | 177565 |
| ru         |  82858 |

### Citation Information

```
@inproceedings{tedeschi-navigli-2022-multinerd,
    title = "{M}ulti{NERD}: A Multilingual, Multi-Genre and Fine-Grained Dataset for Named Entity Recognition (and Disambiguation)",
    author = "Tedeschi, Simone  and
      Navigli, Roberto",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2022",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.findings-naacl.60",
    doi = "10.18653/v1/2022.findings-naacl.60",
    pages = "801--812",
    abstract = "Named Entity Recognition (NER) is the task of identifying named entities in texts and classifying them through specific semantic categories, a process which is crucial for a wide range of NLP applications. Current datasets for NER focus mainly on coarse-grained entity types, tend to consider a single textual genre and to cover a narrow set of languages, thus limiting the general applicability of NER systems.In this work, we design a new methodology for automatically producing NER annotations, and address the aforementioned limitations by introducing a novel dataset that covers 10 languages, 15 NER categories and 2 textual genres.We also introduce a manually-annotated test set, and extensively evaluate the quality of our novel dataset on both this new test set and standard benchmarks for NER.In addition, in our dataset, we include: i) disambiguation information to enable the development of multilingual entity linking systems, and ii) image URLs to encourage the creation of multimodal systems.We release our dataset at https://github.com/Babelscape/multinerd.",
}
```
task_categories:token-classification task_ids:named-entity-recognition multilinguality:multilingual size_categories:<10K language:de language:en language:es language:fr language:it language:nl language:pl language:pt language:ru region:us
{"language": ["de", "en", "es", "fr", "it", "nl", "pl", "pt", "ru"], "multilinguality": ["multilingual"], "size_categories": ["<10K"], "task_categories": ["token-classification"], "task_ids": ["named-entity-recognition"], "pretty_name": "MultiNERD"}
TAGS
#task_categories-token-classification #task_ids-named-entity-recognition #multilinguality-multilingual #size_categories-<10K #language-German #language-English #language-Spanish #language-French #language-Italian #language-Dutch #language-Polish #language-Portuguese #language-Russian #region-us 

Dataset Card for "tner/multinerd"
=================================


Dataset Description
-------------------


* Repository: T-NER
* Paper: URL
* Dataset: MultiNERD
* Domain: Wikipedia, WikiNews
* Number of Entity: 18


### Dataset Summary


MultiNERD NER benchmark dataset formatted in a part of TNER project.


* Entity Types: 'PER', 'LOC', 'ORG', 'ANIM', 'BIO', 'CEL', 'DIS', 'EVE', 'FOOD', 'INST', 'MEDIA', 'PLANT', 'MYTH', 'TIME', 'VEHI', 'MISC', 'SUPER', 'PHY'


Dataset Structure
-----------------


### Data Instances


An example of 'train' of 'de' looks as follows.


### Label ID


The label2id dictionary can be found at here.


### Data Splits




### Dataset Summary


MultiNERD NER benchmark dataset formatted in a part of TNER project.


* Entity Types: 'PER', 'LOC', 'ORG', 'ANIM', 'BIO', 'CEL', 'DIS', 'EVE', 'FOOD', 'INST', 'MEDIA', 'PLANT', 'MYTH', 'TIME', 'VEHI', 'MISC', 'SUPER', 'PHY'


Dataset Structure
----------------- ### Data Instances


An example of 'train' of 'de' looks as follows. ### Label ID


The label2id dictionary can be found at here. ### Data Splits
TAGS
#task_categories-token-classification #task_ids-named-entity-recognition #multilinguality-multilingual #size_categories-<10K #language-German #language-English #language-Spanish #language-French #language-Italian #language-Dutch #language-Polish #language-Portuguese #language-Russian #region-us 
 ### Dataset Summary


MultiNERD NER benchmark dataset formatted in a part of TNER project.


* Entity Types: 'PER', 'LOC', 'ORG', 'ANIM', 'BIO', 'CEL', 'DIS', 'EVE', 'FOOD', 'INST', 'MEDIA', 'PLANT', 'MYTH', 'TIME', 'VEHI', 'MISC', 'SUPER', 'PHY'


Dataset Structure
----------------- ### Data Instances


An example of 'train' of 'de' looks as follows. ### Label ID


The label2id dictionary can be found at here. ### Data Splits
passage: TAGS
#task_categories-token-classification #task_ids-named-entity-recognition #multilinguality-multilingual #size_categories-<10K #language-German #language-English #language-Spanish #language-French #language-Italian #language-Dutch #language-Polish #language-Portuguese #language-Russian #region-us 
### Dataset Summary


MultiNERD NER benchmark dataset formatted in a part of TNER project.


* Entity Types: 'PER', 'LOC', 'ORG', 'ANIM', 'BIO', 'CEL', 'DIS', 'EVE', 'FOOD', 'INST', 'MEDIA', 'PLANT', 'MYTH', 'TIME', 'VEHI', 'MISC', 'SUPER', 'PHY'


Dataset Structure
-----------------### Data Instances


An example of 'train' of 'de' looks as follows.### Label ID


The label2id dictionary can be found at here.### Data Splits