# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: 2016-2025 PyThaiNLP Project
# SPDX-FileType: SOURCE
# SPDX-License-Identifier: Apache-2.0
from typing import List, Union
_tagger = None
_tagger_name = ""
[docs]
def dependency_parsing(
text: str,
model: Union[str, None] = None,
tag: str = "str",
engine: str = "esupar",
) -> Union[List[List[str]], str]:
"""
Dependency Parsing
:param str text: text to apply dependency parsing to
:param str model: model for using with engine \
(for esupar and transformers_ud)
:param str tag: output type (str or list)
:param str engine: the name of dependency parser
:return: str (conllu) or List
:rtype: Union[List[List[str]], str]
**Options for engine**
* *esupar* (default) - Tokenizer, POS tagger and Dependency parser \
using BERT/RoBERTa/DeBERTa models. `GitHub \
<https://github.com/KoichiYasuoka/esupar>`_
* *spacy_thai* - Tokenizer, POS tagger, and dependency parser \
for the Thai language, using Universal Dependencies. \
`GitHub <https://github.com/KoichiYasuoka/spacy-thai>`_
* *transformers_ud* - TransformersUD \
`GitHub <https://github.com/KoichiYasuoka/>`_
* *ud_goeswith* - POS tagging and dependency parsing \
using `goeswith` for subwords
**Options for model (esupar engine)**
* *th* (default) - KoichiYasuoka/roberta-base-thai-spm-upos model \
`Huggingface \
<https://huggingface.co/KoichiYasuoka/roberta-base-thai-spm-upos>`_
* *KoichiYasuoka/deberta-base-thai-upos* - DeBERTa(V2) model \
pre-trained on Thai Wikipedia texts for POS tagging and \
dependency parsing `Huggingface \
<https://huggingface.co/KoichiYasuoka/deberta-base-thai-upos>`_
* *KoichiYasuoka/roberta-base-thai-syllable-upos* - RoBERTa model \
pre-trained on Thai Wikipedia texts for POS tagging and \
dependency parsing. (syllable level) `Huggingface \
<https://huggingface.co/KoichiYasuoka/roberta-base-thai-syllable-upos>`_
* *KoichiYasuoka/roberta-base-thai-char-upos* - RoBERTa model \
pre-trained on Thai Wikipedia texts for POS tagging \
and dependency parsing. (char level) `Huggingface \
<https://huggingface.co/KoichiYasuoka/roberta-base-thai-char-upos>`_
If you want to train models for esupar, you can read \
`Huggingface <https://github.com/KoichiYasuoka/esupar>`_
**Options for model (transformers_ud engine)**
* *KoichiYasuoka/deberta-base-thai-ud-head* (default) - \
DeBERTa(V2) model pretrained on Thai Wikipedia texts \
for dependency parsing (head-detection using Universal \
Dependencies) and question-answering, derived from \
deberta-base-thai. \
trained by th_blackboard.conll. `Huggingface \
<https://huggingface.co/KoichiYasuoka/deberta-base-thai-ud-head>`_
* *KoichiYasuoka/roberta-base-thai-spm-ud-head* - \
roberta model pretrained on Thai Wikipedia texts \
for dependency parsing. `Huggingface \
<https://huggingface.co/KoichiYasuoka/roberta-base-thai-spm-ud-head>`_
**Options for model (ud_goeswith engine)**
* *KoichiYasuoka/deberta-base-thai-ud-goeswith* (default) - \
This is a DeBERTa(V2) model pre-trained on Thai Wikipedia \
texts for POS tagging and dependency parsing (using goeswith for subwords) \
`Huggingface <https://huggingface.co/KoichiYasuoka/deberta-base-thai-ud-goeswith>`_
:Example:
::
from pythainlp.parse import dependency_parsing
print(dependency_parsing("ผมเป็นคนดี", engine="esupar"))
# output:
# 1 ผม _ PRON _ _ 3 nsubj _ SpaceAfter=No
# 2 เป็น _ VERB _ _ 3 cop _ SpaceAfter=No
# 3 คน _ NOUN _ _ 0 root _ SpaceAfter=No
# 4 ดี _ VERB _ _ 3 acl _ SpaceAfter=No
print(dependency_parsing("ผมเป็นคนดี", engine="spacy_thai"))
# output:
# 1 ผม PRON PPRS _ 2 nsubj _ SpaceAfter=No
# 2 เป็น VERB VSTA _ 0 ROOT _ SpaceAfter=No
# 3 คนดี NOUN NCMN _ 2 obj _ SpaceAfter=No
"""
global _tagger, _tagger_name
if _tagger_name != engine:
if engine == "esupar":
from pythainlp.parse.esupar_engine import Parse
_tagger = Parse(model=model)
elif engine == "transformers_ud":
from pythainlp.parse.transformers_ud import Parse
_tagger = Parse(model=model)
elif engine == "spacy_thai":
from pythainlp.parse.spacy_thai_engine import Parse
_tagger = Parse()
elif engine == "ud_goeswith":
from pythainlp.parse.ud_goeswith import Parse
_tagger = Parse(model=model)
else:
raise NotImplementedError("The engine doesn't support.")
_tagger_name = engine
return _tagger(text, tag=tag)