# Copyright 2017 Neural Networks and Deep Learning lab, MIPT
#
# 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.
from typing import Union, List
from sacremoses import MosesDetokenizer, MosesTokenizer
from deeppavlov.core.common.registry import register
from deeppavlov.core.models.component import Component
[docs]@register("nltk_moses_tokenizer")
class NLTKMosesTokenizer(Component):
"""Class for splitting texts on tokens using NLTK wrapper over MosesTokenizer
Attributes:
escape: whether escape characters for use in html markup
tokenizer: tokenizer instance from nltk.tokenize.moses
detokenizer: detokenizer instance from nltk.tokenize.moses
Args:
escape: whether escape characters for use in html markup
"""
def __init__(self, escape: bool = False, *args, **kwargs):
self.escape = escape
self.tokenizer = MosesTokenizer()
self.detokenizer = MosesDetokenizer()
[docs] def __call__(self, batch: List[Union[str, List[str]]]) -> List[Union[List[str], str]]:
"""Tokenize given batch of strings or detokenize given batch of lists of tokens
Args:
batch: list of text samples or list of lists of tokens
Returns:
list of lists of tokens or list of text samples
"""
if isinstance(batch[0], str):
return [self.tokenizer.tokenize(line, escape=self.escape) for line in batch]
else:
return [self.detokenizer.detokenize(line, return_str=True, unescape=self.escape)
for line in batch]