esupar 0.9.3

Tokenizer POS-tagger and Dependency-parser with BERT/RoBERTa models for Japanese Chinese and Thai Language

esupar

Tokenizer, POS-tagger, and dependency-parser with Transformers and SuPar.

Basic usage

>>> import esupar
>>> nlp=esupar.load("ja")
>>> doc=nlp("太郎は花子が読んでいる本を次郎に渡した")
>>> print(doc)
1	太郎	_	PROPN	_	_	12	nsubj	_	SpaceAfter=No
2	は	_	ADP	_	_	1	case	_	SpaceAfter=No
3	花子	_	PROPN	_	_	5	nsubj	_	SpaceAfter=No
4	が	_	ADP	_	_	3	case	_	SpaceAfter=No
5	読ん	_	VERB	_	_	8	acl	_	SpaceAfter=No
6	で	_	SCONJ	_	_	5	mark	_	SpaceAfter=No
7	いる	_	AUX	_	_	5	aux	_	SpaceAfter=No
8	本	_	NOUN	_	_	12	obj	_	SpaceAfter=No
9	を	_	ADP	_	_	8	case	_	SpaceAfter=No
10	次郎	_	PROPN	_	_	12	obl	_	SpaceAfter=No
11	に	_	ADP	_	_	10	case	_	SpaceAfter=No
12	渡し	_	VERB	_	_	0	root	_	SpaceAfter=No
13	た	_	AUX	_	_	12	aux	_	_

>>> import deplacy
>>> deplacy.render(doc,Japanese=True)
太郎 PROPN ═╗<════════╗ nsubj(主語)
は   ADP   <╝         ║ case(格表示)
花子 PROPN ═╗<══╗     ║ nsubj(主語)
が   ADP   <╝   ║     ║ case(格表示)
読ん VERB  ═╗═╗═╝<╗   ║ acl(連体修飾節)
で   SCONJ <╝ ║   ║   ║ mark(標識)
いる AUX   <══╝   ║   ║ aux(動詞補助成分)
本   NOUN  ═╗═════╝<╗ ║ obj(目的語)
を   ADP   <╝       ║ ║ case(格表示)
次郎 PROPN ═╗<╗     ║ ║ obl(斜格補語)
に   ADP   <╝ ║     ║ ║ case(格表示)
渡し VERB  ═╗═╝═════╝═╝ root(親)
た   AUX   <╝           aux(動詞補助成分)

esupar.load(model) loads a natural language processor pipeline, working on Universal Dependencies. Available model options are:

• model="ja" Japanese model bert-base-japanese-upos (default)
• model="ja_large" Japanese model bert-large-japanese-upos
• model="ja_luw" Japanese long-unit-word model roberta-small-japanese-luw-upos
• model="ja_luw_base" Japanese long-unit-word model bert-base-japanese-luw-upos
• model="ja_luw_large" Japanese long-unit-word model bert-large-japanese-luw-upos
• model="zh" Chinese model chinese-bert-wwm-ext-upos
• model="zh_base" Chinese model chinese-roberta-base-upos
• model="zh_large" Chinese model chinese-roberta-large-upos
• model="lzh" Classical Chinese model roberta-classical-chinese-base-upos
• model="lzh_large" Classical Chinese model roberta-classical-chinese-large-upos
• model="th" Thai model roberta-base-thai-syllable-upos

Installation for Linux

pip3 install esupar --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, and then:

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

Installation for Google Colaboratory

!pip install esupar

Try notebook.

Author

Koichi Yasuoka (安岡孝一)