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79 lines
2.3 KiB
79 lines
2.3 KiB
from __future__ import annotations
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import collections
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import json
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import os
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import string
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from collections.abc import Iterable
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from .WordTokenizer import ENGLISH_STOP_WORDS, WordTokenizer
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class WhitespaceTokenizer(WordTokenizer):
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"""
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Simple and fast white-space tokenizer. Splits sentence based on white spaces.
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Punctuation are stripped from tokens.
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"""
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def __init__(
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self, vocab: Iterable[str] = [], stop_words: Iterable[str] = ENGLISH_STOP_WORDS, do_lower_case: bool = False
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):
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self.stop_words = set(stop_words)
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self.do_lower_case = do_lower_case
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self.set_vocab(vocab)
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def get_vocab(self):
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return self.vocab
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def set_vocab(self, vocab: Iterable[str]):
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self.vocab = vocab
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self.word2idx = collections.OrderedDict([(word, idx) for idx, word in enumerate(vocab)])
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def tokenize(self, text: str, **kwargs) -> list[int]:
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if self.do_lower_case:
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text = text.lower()
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tokens = text.split()
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tokens_filtered = []
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for token in tokens:
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if token in self.stop_words:
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continue
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elif token in self.word2idx:
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tokens_filtered.append(self.word2idx[token])
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continue
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token = token.strip(string.punctuation)
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if token in self.stop_words:
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continue
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elif len(token) > 0 and token in self.word2idx:
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tokens_filtered.append(self.word2idx[token])
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continue
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token = token.lower()
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if token in self.stop_words:
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continue
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elif token in self.word2idx:
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tokens_filtered.append(self.word2idx[token])
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continue
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return tokens_filtered
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def save(self, output_path: str):
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with open(os.path.join(output_path, "whitespacetokenizer_config.json"), "w") as fOut:
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json.dump(
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{
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"vocab": list(self.word2idx.keys()),
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"stop_words": list(self.stop_words),
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"do_lower_case": self.do_lower_case,
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},
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fOut,
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)
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@staticmethod
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def load(input_path: str):
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with open(os.path.join(input_path, "whitespacetokenizer_config.json")) as fIn:
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config = json.load(fIn)
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return WhitespaceTokenizer(**config)
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