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diff --git a/README.md b/README.md
deleted file mode 100644
index 8db80624dea3e813ae72fa76d696b2343f0b61ed..0000000000000000000000000000000000000000
--- a/README.md
+++ /dev/null
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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.",
-}
-```
\ No newline at end of file
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diff --git a/wikineural.py b/wikineural.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 = """[wikineural](https://aclanthology.org/2021.findings-emnlp.215/)"""
-_NAME = "wikineural"
-_VERSION = "1.0.0"
-_CITATION = """
-@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.",
-}
-"""
-
-_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}/test.jsonl'],
- str(datasets.Split.TRAIN): [f'{_URL}/{l}/train.jsonl'],
- str(datasets.Split.VALIDATION): [f'{_URL}/{l}/dev.jsonl']
- } for l in _LANGUAGE
-}
-
-
-class WikiNeuralConfig(datasets.BuilderConfig):
- """BuilderConfig"""
-
- def __init__(self, **kwargs):
- """BuilderConfig.
-
- Args:
- **kwargs: keyword arguments forwarded to super.
- """
- super(WikiNeuralConfig, self).__init__(**kwargs)
-
-
-class WikiNeural(datasets.GeneratorBasedBuilder):
- """Dataset."""
-
- BUILDER_CONFIGS = [
- WikiNeuralConfig(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.TRAIN, datasets.Split.VALIDATION, 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,
- )
\ No newline at end of file