Source code for deeppavlov.dataset_readers.siamese_reader

# 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
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# 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
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import csv
from pathlib import Path
from typing import Dict, List, Tuple

from deeppavlov.core.commands.utils import expand_path
from deeppavlov.core.common.registry import register
from deeppavlov.core.data.dataset_reader import DatasetReader


[docs]@register('siamese_reader') class SiameseReader(DatasetReader): """The class to read dataset for ranking or paraphrase identification with Siamese networks.""" def read(self, data_path: str, **kwargs) -> Dict[str, List[Tuple[List[str], int]]]: """Read the dataset for ranking or paraphrase identification with Siamese networks. Args: data_path: A path to a folder with dataset files. """ dataset = {'train': None, 'valid': None, 'test': None} data_path = expand_path(data_path) train_fname = data_path / 'train.csv' valid_fname = data_path / 'valid.csv' test_fname = data_path / 'test.csv' dataset["train"] = self._preprocess_data_train(train_fname) dataset["valid"] = self._preprocess_data_valid_test(valid_fname) dataset["test"] = self._preprocess_data_valid_test(test_fname) return dataset def _preprocess_data_train(self, fname: Path) -> List[Tuple[List[str], int]]: data = [] with open(fname, 'r') as f: reader = csv.reader(f, delimiter='\t') for el in reader: data.append((el[:2], int(el[2]))) return data def _preprocess_data_valid_test(self, fname: Path) -> List[Tuple[List[str], int]]: data = [] with open(fname, 'r') as f: reader = csv.reader(f, delimiter='\t') for el in reader: data.append((el, 1)) return data