update

.gitattributes

@@ -49,3 +49,30 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
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README.md

@@ -0,0 +1,248 @@
+---
+language:
+- ace
+- bg
+- da
+- fur
+- ilo
+- lij
+- mzn
+- qu
+- su
+- vi
+- af
+- bh
+- de
+- fy
+- io
+- lmo
+- nap
+- rm
+- sv
+- vls
+- als
+- bn
+- diq
+- ga
+- is
+- ln
+- nds
+- ro
+- sw
+- vo
+- am
+- bo
+- dv
+- gan
+- it
+- lt
+- ne
+- ru
+- szl
+- wa
+- an
+- br
+- el
+- gd
+- ja
+- lv
+- nl
+- rw
+- ta
+- war
+- ang
+- bs
+- eml
+- gl
+- jbo
+- nn
+- sa
+- te
+- wuu
+- ar
+- ca
+- en
+- gn
+- jv
+- mg
+- no
+- sah
+- tg
+- xmf
+- arc
+- eo
+- gu
+- ka
+- mhr
+- nov
+- scn
+- th
+- yi
+- arz
+- cdo
+- es
+- hak
+- kk
+- mi
+- oc
+- sco
+- tk
+- yo
+- as
+- ce
+- et
+- he
+- km
+- min
+- or
+- sd
+- tl
+- zea
+- ast
+- ceb
+- eu
+- hi
+- kn
+- mk
+- os
+- sh
+- tr
+- ay
+- ckb
+- ext
+- hr
+- ko
+- ml
+- pa
+- si
+- tt
+- az
+- co
+- fa
+- hsb
+- ksh
+- mn
+- pdc
+- ug
+- ba
+- crh
+- fi
+- hu
+- ku
+- mr
+- pl
+- sk
+- uk
+- zh
+- bar
+- cs
+- hy
+- ky
+- ms
+- pms
+- sl
+- ur
+- csb
+- fo
+- ia
+- la
+- mt
+- pnb
+- so
+- uz
+- cv
+- fr
+- id
+- lb
+- mwl
+- ps
+- sq
+- vec
+- be
+- cy
+- frr
+- ig
+- li
+- my
+- pt
+- sr
+multilinguality:
+- multilingual
+size_categories:
+- 10K<100k
+task_categories:
+- token-classification
+task_ids:
+- named-entity-recognition
+pretty_name: WikiAnn
+---
+
+# Dataset Card for "tner/wikiann"
+
+## Dataset Description
+
+- **Repository:** [T-NER](https://github.com/asahi417/tner)
+- **Paper:** [https://aclanthology.org/P17-1178/](https://aclanthology.org/P17-1178/)
+- **Dataset:** WikiAnn
+- **Domain:** Wikipedia
+- **Number of Entity:** 3
+
+
+### Dataset Summary
+WikiAnn NER dataset formatted in a part of [TNER](https://github.com/asahi417/tner) project.
+- Entity Types: `LOC`, `ORG`, `PER`
+
+## 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/btc/raw/main/dataset/label.json).
+```python
+{
+    "B-LOC": 0,
+    "B-ORG": 1,
+    "B-PER": 2,
+    "I-LOC": 3,
+    "I-ORG": 4,
+    "I-PER": 5,
+    "O": 6
+}
+```
+
+### Data Splits
+
+|  name   |train|validation|test|
+|---------|----:|---------:|---:|
+|btc      | 6338|      1001|2000|
+
+### Citation Information
+
+```
+@inproceedings{pan-etal-2017-cross,
+    title = "Cross-lingual Name Tagging and Linking for 282 Languages",
+    author = "Pan, Xiaoman  and
+      Zhang, Boliang  and
+      May, Jonathan  and
+      Nothman, Joel  and
+      Knight, Kevin  and
+      Ji, Heng",
+    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
+    month = jul,
+    year = "2017",
+    address = "Vancouver, Canada",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/P17-1178",
+    doi = "10.18653/v1/P17-1178",
+    pages = "1946--1958",
+    abstract = "The ambitious goal of this work is to develop a cross-lingual name tagging and linking framework for 282 languages that exist in Wikipedia. Given a document in any of these languages, our framework is able to identify name mentions, assign a coarse-grained or fine-grained type to each mention, and link it to an English Knowledge Base (KB) if it is linkable. We achieve this goal by performing a series of new KB mining methods: generating {``}silver-standard{''} annotations by transferring annotations from English to other languages through cross-lingual links and KB properties, refining annotations through self-training and topic selection, deriving language-specific morphology features from anchor links, and mining word translation pairs from cross-lingual links. Both name tagging and linking results for 282 languages are promising on Wikipedia data and on-Wikipedia data.",
+}
+```

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wikineural.py

@@ -0,0 +1,100 @@
+""" 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 = """[WikiAnn](https://aclanthology.org/P17-1178/)"""
+_NAME = "wikiann"
+_VERSION = "1.1.0"
+_CITATION = """
+@inproceedings{pan-etal-2017-cross,
+    title = "Cross-lingual Name Tagging and Linking for 282 Languages",
+    author = "Pan, Xiaoman  and
+      Zhang, Boliang  and
+      May, Jonathan  and
+      Nothman, Joel  and
+      Knight, Kevin  and
+      Ji, Heng",
+    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
+    month = jul,
+    year = "2017",
+    address = "Vancouver, Canada",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/P17-1178",
+    doi = "10.18653/v1/P17-1178",
+    pages = "1946--1958",
+    abstract = "The ambitious goal of this work is to develop a cross-lingual name tagging and linking framework for 282 languages that exist in Wikipedia. Given a document in any of these languages, our framework is able to identify name mentions, assign a coarse-grained or fine-grained type to each mention, and link it to an English Knowledge Base (KB) if it is linkable. We achieve this goal by performing a series of new KB mining methods: generating {``}silver-standard{''} annotations by transferring annotations from English to other languages through cross-lingual links and KB properties, refining annotations through self-training and topic selection, deriving language-specific morphology features from anchor links, and mining word translation pairs from cross-lingual links. Both name tagging and linking results for 282 languages are promising on Wikipedia data and on-Wikipedia data.",
+}
+"""
+
+_HOME_PAGE = "https://github.com/asahi417/tner"
+_URL = f'https://huggingface.co/datasets/tner/{_NAME}/resolve/main/dataset'
+_LANGUAGE = ["ace", "bg", "da", "fur", "ilo", "lij", "mzn", "qu", "su", "vi", "af", "bh", "de", "fy", "io", "lmo", "nap",
+        "rm", "sv", "vls", "als", "bn", "diq", "ga", "is", "ln", "nds", "ro", "sw", "vo", "am", "bo", "dv", "gan", "it",
+        "lt", "ne", "ru", "szl", "wa", "an", "br", "el", "gd", "ja", "lv", "nl", "rw", "ta", "war", "ang", "bs", "eml",
+        "gl", "jbo", "map-bms", "nn", "sa", "te", "wuu", "ar", "ca", "en", "gn", "jv", "mg", "no", "sah", "tg", "xmf",
+        "arc", "cbk-zam", "eo", "gu", "ka", "mhr", "nov", "scn", "th", "yi", "arz", "cdo", "es", "hak", "kk", "mi",
+        "oc", "sco", "tk", "yo", "as", "ce", "et", "he", "km", "min", "or", "sd", "tl", "zea", "ast", "ceb", "eu", "hi",
+        "kn", "mk", "os", "sh", "tr", "zh-classical", "ay", "ckb", "ext", "hr", "ko", "ml", "pa", "si", "tt",
+        "zh-min-nan", "az", "co", "fa", "hsb", "ksh", "mn", "pdc", "simple", "ug", "zh-yue", "ba", "crh", "fi", "hu",
+        "ku", "mr", "pl", "sk", "uk", "zh", "bar", "cs", "fiu-vro", "hy", "ky", "ms", "pms", "sl", "ur", "bat-smg",
+        "csb", "fo", "ia", "la", "mt", "pnb", "so", "uz", "be-x-old", "cv", "fr", "id", "lb", "mwl", "ps", "sq", "vec",
+        "be", "cy", "frr", "ig", "li", "my", "pt", "sr", "vep"]
+_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 WikiAnnConfig(datasets.BuilderConfig):
+    """BuilderConfig"""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig.
+
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(WikiAnnConfig, self).__init__(**kwargs)
+
+
+class WikiAnn(datasets.GeneratorBasedBuilder):
+    """Dataset."""
+
+    BUILDER_CONFIGS = [
+        WikiAnnConfig(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,
+        )