| |
| |
| |
| |
| """ |
| 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 |
|
|
