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README.md

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----
-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
----
-
-# 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.",
-}
-```

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dataset/label.json

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-{"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}

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it/multinerd-test.parquet

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multinerd.py

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-""" NER dataset compiled by T-NER library https://github.com/asahi417/tner/tree/master/tner """
-import json
-from itertools import chain
-import datasets
-
-logger = datasets.logging.get_logger(__name__)
-_DESCRIPTION = """[MultiNERD](https://aclanthology.org/2022.findings-naacl.60/)"""
-_NAME = "multinerd"
-_VERSION = "1.0.0"
-_CITATION = """
-@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.",
-}
-"""
-
-_HOME_PAGE = "https://github.com/asahi417/tner"
-_URL = f'https://huggingface.co/datasets/tner/{_NAME}/resolve/main/dataset'
-_LANGUAGE = ['de', 'en', 'es', 'fr', 'it', 'nl', 'pl', 'pt', 'ru']
-_URLS = {
-    l: {
-      str(datasets.Split.TEST): [f'{_URL}/{l}.jsonl'],
-     } for l in _LANGUAGE
-}
-
-
-class MultiNERDConfig(datasets.BuilderConfig):
-    """BuilderConfig"""
-
-    def __init__(self, **kwargs):
-        """BuilderConfig.
-
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(MultiNERDConfig, self).__init__(**kwargs)
-
-
-class MultiNERD(datasets.GeneratorBasedBuilder):
-    """Dataset."""
-
-    BUILDER_CONFIGS = [
-        MultiNERDConfig(name=l, version=datasets.Version(_VERSION), description=f"{_DESCRIPTION} (language: {l})") for l in _LANGUAGE
-    ]
-
-    def _split_generators(self, dl_manager):
-        downloaded_file = dl_manager.download_and_extract(_URLS[self.config.name])
-        return [datasets.SplitGenerator(name=i, gen_kwargs={"filepaths": downloaded_file[str(i)]})
-                for i in [datasets.Split.TEST]]
-
-    def _generate_examples(self, filepaths):
-        _key = 0
-        for filepath in filepaths:
-            logger.info(f"generating examples from = {filepath}")
-            with open(filepath, encoding="utf-8") as f:
-                _list = [i for i in f.read().split('\n') if len(i) > 0]
-                for i in _list:
-                    data = json.loads(i)
-                    yield _key, data
-                    _key += 1
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "tokens": datasets.Sequence(datasets.Value("string")),
-                    "tags": datasets.Sequence(datasets.Value("int32")),
-                }
-            ),
-            supervised_keys=None,
-            homepage=_HOME_PAGE,
-            citation=_CITATION,
-        )

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