from __future__ import annotations
import numpy as np
from torch.utils.data import Dataset
from transformers.utils.import_utils import NLTK_IMPORT_ERROR, is_nltk_available
from sentence_transformers.readers.InputExample import InputExample
class DenoisingAutoEncoderDataset(Dataset):
"""
The DenoisingAutoEncoderDataset returns InputExamples in the format: texts=[noise_fn(sentence), sentence]
It is used in combination with the DenoisingAutoEncoderLoss: Here, a decoder tries to re-construct the
sentence without noise.
Args:
sentences: A list of sentences
noise_fn: A noise function: Given a string, it returns a string
with noise, e.g. deleted words
"""
def __init__(self, sentences: list[str], noise_fn=lambda s: DenoisingAutoEncoderDataset.delete(s)):
if not is_nltk_available():
raise ImportError(NLTK_IMPORT_ERROR.format(self.__class__.__name__))
self.sentences = sentences
self.noise_fn = noise_fn
def __getitem__(self, item):
sent = self.sentences[item]
return InputExample(texts=[self.noise_fn(sent), sent])
def __len__(self):
return len(self.sentences)
# Deletion noise.
@staticmethod
def delete(text, del_ratio=0.6):
from nltk import word_tokenize
from nltk.tokenize.treebank import TreebankWordDetokenizer
words = word_tokenize(text)
n = len(words)
if n == 0:
return text
keep_or_not = np.random.rand(n) > del_ratio
if sum(keep_or_not) == 0:
keep_or_not[np.random.choice(n)] = True # guarantee that at least one word remains
words_processed = TreebankWordDetokenizer().detokenize(np.array(words)[keep_or_not])
return words_processed