# 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 pathlib import Path
import pickle
from deeppavlov.core.data.dataset_reader import DatasetReader
from deeppavlov.core.data.utils import download
from deeppavlov.core.common.registry import register
[docs]@register('ontonotes_reader')
class OntonotesReader(DatasetReader):
"""Class to read training datasets in OntoNotes format"""
URL = 'http://files.deeppavlov.ai/datasets/ontonotes_senna.pckl'
def read(self, data_path, file_name: str='ontonotes_senna.pckl', provide_senna_pos=False, provide_senna_ner=False):
path = Path(data_path).resolve() / file_name
if not path.exists():
download(str(path), self.URL)
with open(path, 'rb') as f:
dataset = pickle.load(f)
dataset_filtered = {}
for key, data in dataset.items():
dataset_filtered[key] = []
for (toks, pos, ner), tags in data:
if not provide_senna_pos and not provide_senna_ner:
dataset_filtered[key].append((toks, tags))
else:
x = [toks]
if provide_senna_pos:
x.append(pos)
if provide_senna_ner:
x.append(ner)
dataset_filtered[key].append((x, tags))
return dataset_filtered