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SuPar-Kanbun

Tokenizer, POS-Tagger and Dependency-Parser for Classical Chinese Texts (漢文/文言文) with spaCy, Transformers and SuPar.

Basic usage

>>> import suparkanbun
>>> nlp=suparkanbun.load()
>>> doc=nlp("不入虎穴不得虎子")
>>> print(type(doc))
<class 'spacy.tokens.doc.Doc'>
>>> print(suparkanbun.to_conllu(doc))
# text = 不入虎穴不得虎子
1	不	不	ADV	v,副詞,否定,無界	Polarity=Neg	2	advmod	_	Gloss=not|SpaceAfter=No
2	入	入	VERB	v,動詞,行為,移動	_	0	root	_	Gloss=enter|SpaceAfter=No
3	虎	虎	NOUN	n,名詞,主体,動物	_	4	nmod	_	Gloss=tiger|SpaceAfter=No
4	穴	穴	NOUN	n,名詞,固定物,地形	Case=Loc	2	obj	_	Gloss=cave|SpaceAfter=No
5	不	不	ADV	v,副詞,否定,無界	Polarity=Neg	6	advmod	_	Gloss=not|SpaceAfter=No
6	得	得	VERB	v,動詞,行為,得失	_	2	parataxis	_	Gloss=get|SpaceAfter=No
7	虎	虎	NOUN	n,名詞,主体,動物	_	8	nmod	_	Gloss=tiger|SpaceAfter=No
8	子	子	NOUN	n,名詞,人,関係	_	6	obj	_	Gloss=child|SpaceAfter=No

>>> import deplacy
>>> deplacy.render(doc)
ADV  <════╗   advmod
VERB ═══╗═╝═╗ ROOT
NOUN <╗ ║   ║ nmod
NOUN ═╝<╝   ║ obj
ADV  <════╗ ║ advmod
VERB ═══╗═╝<╝ parataxis
NOUN <╗ ║     nmod
NOUN ═╝<╝     obj

suparkanbun.load() has two options suparkanbun.load(BERT="roberta-classical-chinese-base-char",Danku=False). With the option Danku=True the pipeline tries to segment sentences automatically. Available BERT options are:

Installation for Linux

pip3 install suparkanbun --user

Installation for Cygwin64

Make sure to get python37-devel python37-pip python37-cython python37-numpy python37-wheel gcc-g++ mingw64-x86_64-gcc-g++ git curl make cmake packages, and then:

curl -L https://raw.githubusercontent.com/KoichiYasuoka/CygTorch/master/installer/supar.sh | sh
pip3.7 install suparkanbun --no-build-isolation

Installation for Jupyter Notebook (Google Colaboratory)

!pip install suparkanbun 

Try notebook for Google Colaboratory.

Author

Koichi Yasuoka (安岡孝一)

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This model can be loaded on the Inference API on-demand.

Dataset used to train KoichiYasuoka/SuPar-Kanbun