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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<100k
-task_categories:
-- token-classification
-task_ids:
-- named-entity-recognition
-pretty_name: WikiNeural
----
-
-# Dataset Card for "tner/wikineural"
-
-## Dataset Description
-
-- **Repository:** [T-NER](https://github.com/asahi417/tner)
-- **Paper:** [https://aclanthology.org/2021.findings-emnlp.215/](https://aclanthology.org/2021.findings-emnlp.215/)
-- **Dataset:** WikiNeural
-- **Domain:** Wikipedia
-- **Number of Entity:** 16
-
-
-### Dataset Summary
-WikiAnn NER 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`
-
-## Dataset Structure
-
-### Data Instances
-An example of `train` of `de` looks as follows.
-
-```
-{
-    'tokens': [ "Dieses", "wiederum", "basierte", "auf", "dem", "gleichnamigen", "Roman", "von", "Noël", "Calef", "." ],
-    'tags': [ 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0 ]
-}
-```
-
-### Label ID
-The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/wikineural/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-MISC": 31,
-"I-MISC": 32
-}
-```
-
-### Data Splits
-
-| language   |   train |   validation |   test |
-|:-----------|--------:|-------------:|-------:|
-| de         |   98640 |        12330 |  12372 |
-| en         |   92720 |        11590 |  11597 |
-| es         |   76320 |         9540 |   9618 |
-| fr         |  100800 |        12600 |  12678 |
-| it         |   88400 |        11050 |  11069 |
-| nl         |   83680 |        10460 |  10547 |
-| pl         |  108160 |        13520 |  13585 |
-| pt         |   80560 |        10070 |  10160 |
-| ru         |   92320 |        11540 |  11580 |
-
-### Citation Information
-
-```
-@inproceedings{tedeschi-etal-2021-wikineural-combined,
-    title = "{W}iki{NE}u{R}al: {C}ombined Neural and Knowledge-based Silver Data Creation for Multilingual {NER}",
-    author = "Tedeschi, Simone  and
-      Maiorca, Valentino  and
-      Campolungo, Niccol{\`o}  and
-      Cecconi, Francesco  and
-      Navigli, Roberto",
-    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
-    month = nov,
-    year = "2021",
-    address = "Punta Cana, Dominican Republic",
-    publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/2021.findings-emnlp.215",
-    doi = "10.18653/v1/2021.findings-emnlp.215",
-    pages = "2521--2533",
-    abstract = "Multilingual Named Entity Recognition (NER) is a key intermediate task which is needed in many areas of NLP. In this paper, we address the well-known issue of data scarcity in NER, especially relevant when moving to a multilingual scenario, and go beyond current approaches to the creation of multilingual silver data for the task. We exploit the texts of Wikipedia and introduce a new methodology based on the effective combination of knowledge-based approaches and neural models, together with a novel domain adaptation technique, to produce high-quality training corpora for NER. We evaluate our datasets extensively on standard benchmarks for NER, yielding substantial improvements up to 6 span-based F1-score points over previous state-of-the-art systems for data creation.",
-}
-```

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