File size: 2,329 Bytes
e4b9a7b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 | # -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: 2016-2025 PyThaiNLP Project
# SPDX-FileType: SOURCE
# SPDX-License-Identifier: Apache-2.0
"""
Tokenzier classes for ULMFiT
"""
from typing import Collection, List
from pythainlp.tokenize import THAI2FIT_TOKENIZER
class BaseTokenizer:
"""Basic class for a tokenizer function. (codes from `fastai`)"""
def __init__(self, lang: str):
self.lang = lang
def tokenizer(self, t: str) -> List[str]:
return t.split(" ")
def add_special_cases(self, toks: Collection[str]):
pass
class ThaiTokenizer(BaseTokenizer):
"""
Wrapper around a frozen newmm tokenizer to make it a
:class:`fastai.BaseTokenizer`.
(see: https://docs.fast.ai/text.transform#BaseTokenizer)
"""
def __init__(self, lang: str = "th"):
self.lang = lang
@staticmethod
def tokenizer(text: str) -> List[str]:
"""
This function tokenizes text using *newmm* engine and the dictionary
specifically for `ulmfit` related functions
(see: `Dictionary file (.txt) \
<https://github.com/PyThaiNLP/pythainlp/blob/dev/pythainlp/corpus/words_th_thai2fit_201810.txt>`_).
:meth: tokenize text using a frozen newmm engine
:param str text: text to tokenize
:return: tokenized text
:rtype: list[str]
:Example:
Using :func:`pythainlp.ulmfit.ThaiTokenizer.tokenizer` is
similar to :func:`pythainlp.tokenize.word_tokenize`
using *ulmfit* engine.
>>> from pythainlp.ulmfit import ThaiTokenizer
>>> from pythainlp.tokenize import word_tokenize
>>>
>>> text = "อาภรณ์, จินตมยปัญญา ภาวนามยปัญญา"
>>> ThaiTokenizer.tokenizer(text)
['อาภรณ์', ',', ' ', 'จิน', 'ตม', 'ย', 'ปัญญา',
' ', 'ภาวนามยปัญญา']
>>>
>>> word_tokenize(text, engine='ulmfit')
['อาภรณ์', ',', ' ', 'จิน', 'ตม', 'ย', 'ปัญญา',
' ', 'ภาวนามยปัญญา']
"""
return THAI2FIT_TOKENIZER.word_tokenize(text)
def add_special_cases(self, toks):
pass
|