Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,103 @@
+---
+tags:
+- named-entity-recognition
+language:
+- ind
+- eng
+- jav
+- min
+- sun
+- ace
+- mly
+---
+
+# wikiann
+
+The wikiann dataset contains NER tags with labels from O (0), B-PER (1), I-PER (2), B-ORG (3), I-ORG (4), B-LOC (5), I-LOC (6). The Indonesian subset is used.
+
+WikiANN (sometimes called PAN-X) is a multilingual named entity recognition dataset consisting of Wikipedia articles
+
+ annotated with LOC (location), PER (person), and ORG (organisation)
+
+ tags in the IOB2 format. This version corresponds to the balanced train, dev, and test splits of
+
+  Rahimi et al. (2019), and uses the following subsets from the original WikiANN corpus
+
+
+
+Language	WikiAnn	ISO 639-3
+
+Indonesian	id	ind
+
+Javanese	jv	jav
+
+Minangkabau	min	min
+
+Sundanese	su	sun
+
+Acehnese	ace	ace
+
+Malay	ms	mly
+
+Banyumasan	map-bms	map-bms
+
+## Dataset Usage
+
+Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`.
+
+## 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://www.aclweb.org/anthology/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.",
+}
+@inproceedings{rahimi-etal-2019-massively,
+    title = "Massively Multilingual Transfer for {NER}",
+    author = "Rahimi, Afshin  and
+      Li, Yuan  and
+      Cohn, Trevor",
+    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
+    month = jul,
+    year = "2019",
+    address = "Florence, Italy",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/P19-1015",
+    pages = "151--164",
+}
+```
+
+## License
+
+Apache-2.0 license
+
+## Homepage
+
+[https://github.com/afshinrahimi/mmner](https://github.com/afshinrahimi/mmner)
+
+### NusaCatalogue
+
+For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue)