# 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.
import json
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("paraphraser_pretrain_reader")
class ParaphraserPretrainReader(DatasetReader):
"""The class to read the pretraining dataset for the paraphrase identification task from files."""
def read(self,
data_path: str,
seed: int = None, *args, **kwargs) -> Dict[str, List[Tuple[List[str], int]]]:
"""Read the pretraining dataset for the paraphrase identification task from files.
Args:
data_path: A path to a folder with dataset files.
seed: Random seed.
"""
data_path = expand_path(data_path)
train_fname = data_path / 'paraphraser_pretrain_train.json'
test_fname = data_path / 'paraphraser_pretrain_val.json'
train_data = self.build_data(train_fname)
test_data = self.build_data(test_fname)
dataset = {"train": train_data, "valid": test_data, "test": test_data}
return dataset
def int_class(self, str_y):
if str_y == '-1':
return 0
else:
return 1
def build_data(self, name):
with open(name) as f:
data = json.load(f)
return [([doc['text_1'], doc['text_2']], self.int_class(doc['class'])) for doc in data]