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.gitattributes

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 *.jpg filter=lfs diff=lfs merge=lfs -text
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+dataset/pt.jsonl filter=lfs diff=lfs merge=lfs -text
+dataset/es.jsonl filter=lfs diff=lfs merge=lfs -text
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+dataset/nl.jsonl filter=lfs diff=lfs merge=lfs -text
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README.md

@@ -0,0 +1,124 @@
+---
+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` looks as follows.
+
+```
+{
+    'tokens': ['I', 'hate', 'the', 'words', 'chunder', ',', 'vomit', 'and', 'puke', '.', 'BUUH', '.'],
+    'tags': [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6]
+}
+```
+
+### 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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multinerd.py

@@ -0,0 +1,89 @@
+""" 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,
+        )