# Copyright 2021-2024 Cambridge Quantum Computing Ltd.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Spacy Tokeniser
===============
A tokeniser that wraps SpaCy.
"""
from __future__ import annotations
__all__ = ['SpacyTokeniser']
from collections.abc import Iterable
import logging
from typing import TYPE_CHECKING
from lambeq.tokeniser import Tokeniser
if TYPE_CHECKING:
import spacy
import spacy.cli
def _import_spacy() -> None:
global spacy
import spacy
import spacy.lang.en
import spacy.cli
[docs]class SpacyTokeniser(Tokeniser):
"""Tokeniser class based on SpaCy."""
[docs] def __init__(self) -> None:
_import_spacy()
try:
self.tokeniser = spacy.load('en_core_web_sm')
except OSError:
logger = logging.getLogger(__name__)
logger.warning('Downloading SpaCy tokeniser. '
'This action only has to happen once.')
spacy.cli.download('en_core_web_sm')
self.tokeniser = spacy.load('en_core_web_sm')
self.spacy_nlp = spacy.lang.en.English()
self.spacy_nlp.add_pipe('sentencizer')
[docs] def split_sentences(self, text: str) -> list[str]:
"""Split input text into a list of sentences.
Parameters
----------
text : str
A single string that contains one or multiple sentences.
Returns
-------
list of str
List of sentences, one sentence in each string.
"""
return [str(sent) for sent in self.spacy_nlp(text).sents]
[docs] def tokenise_sentences(self, sentences: Iterable[str]) -> list[list[str]]:
"""Tokenise a list of sentences.
Parameters
----------
sentences : list of str
A list of untokenised sentences.
Returns
-------
list of list of str
A list of tokenised sentences, where each sentence is a list
of tokens.
"""
disable = ['parser', 'tagger', 'ner', 'lemmatizer']
tokenised = []
for s in sentences:
s_cleaned = ' '.join(s.split())
tokenised.append([
str(t) for t in self.tokeniser(s_cleaned, disable=disable)
])
return tokenised