# Copyright 2022 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================
"""
CoLA load function
"""
# pylint: disable=C0103
import os
from typing import Union, Tuple
from mindspore.dataset import GeneratorDataset
from mindnlp.utils.download import cache_file
from mindnlp.dataset.register import load, process
from mindnlp.dataset.process import common_process
from mindnlp.dataset.transforms import BasicTokenizer
from mindnlp.configs import DEFAULT_ROOT
from mindnlp.utils import unzip
URL = "https://nyu-mll.github.io/CoLA/cola_public_1.1.zip"
MD5 = "9f6d88c3558ec424cd9d66ea03589aba"
[docs]class Cola:
"""
CoLA dataset source
"""
def __init__(self, path) -> None:
self.path: str = path
self._source, self._label, self._sentence = [], [], []
self._load()
def _load(self):
with open(self.path, "r", encoding="utf-8") as f:
dataset = f.read()
lines = dataset.split("\n")
if not self.path.endswith("out_of_domain_dev.tsv"):
lines.pop(len(lines) - 1)
for line in lines:
l = line.split("\t")
self._source.append(l[0])
self._label.append(l[1])
self._sentence.append(l[-1])
def __getitem__(self, index):
return self._source[index], self._label[index], self._sentence[index]
def __len__(self):
return len(self._sentence)
[docs]@load.register
def CoLA(
root: str = DEFAULT_ROOT, split: Union[Tuple[str], str] = ("train", "dev", "test"), proxies=None
):
r"""
Load the CoLA dataset
Args:
root (str): Directory where the datasets are saved.
Default:~/.mindnlp
split (str|Tuple[str]): Split or splits to be returned.
Default:('train', 'dev', 'test').
proxies (dict): a dict to identify proxies,for example: {"https": "https://127.0.0.1:7890"}.
Returns:
- **datasets_list** (list) -A list of loaded datasets.
If only one type of dataset is specified,such as 'trian',
this dataset is returned instead of a list of datasets.
Examples:
>>> root = "~/.mindnlp"
>>> split = ('train', 'dev', 'test')
>>> dataset_train,dataset_dev,dataset_test = CoLA(root, split)
>>> train_iter = dataset_train.create_tuple_iterator()
>>> print(next(train_iter))
[Tensor(shape=[], dtype=String, value= 'gj04'), Tensor(shape=[], dtype=String, \
\value= '1'), \Tensor(shape=[], dtype=String, value= "Our friends won't buy \
this analysis, let alone the \next one we propose.")]
"""
cache_dir = os.path.join(root, "datasets", "CoLA")
path_dict = {
"train": "in_domain_train.tsv",
"dev": "in_domain_dev.tsv",
"test": "out_of_domain_dev.tsv",
}
column_names = ["source", "label", "sentence"]
path_list = []
datasets_list = []
path, _ = cache_file(None, url=URL, cache_dir=cache_dir, md5sum=MD5, proxies=proxies)
unzip(path, cache_dir)
if isinstance(split, str):
path_list.append(
os.path.join(cache_dir, "cola_public", "raw", path_dict[split])
)
else:
for s in split:
path_list.append(
os.path.join(cache_dir, "cola_public", "raw", path_dict[s])
)
for path in path_list:
datasets_list.append(
GeneratorDataset(
source=Cola(path), column_names=column_names, shuffle=False
)
)
if len(path_list) == 1:
return datasets_list[0]
return datasets_list
[docs]@process.register
def CoLA_Process(dataset, column="sentence", tokenizer=BasicTokenizer(), vocab=None):
"""
the process of the CoLA dataset
Args:
dataset (GeneratorDataset): CoLA dataset.
column (str): the column needed to be transpormed of the CoLA dataset.
tokenizer (TextTensorOperation): tokenizer you choose to tokenize the text dataset.
vocab (Vocab): vocabulary object, used to store the mapping of token and index.
Returns:
- **dataset** (MapDataset) - dataset after transforms.
- **Vocab** (Vocab) - vocab created from dataset
Raises:
TypeError: If `input_column` is not a string.
Examples:
>>> from mindnlp.dataset import CoLA, CoLA_Process
>>> train_dataset, dataset_dev, dataset_test = CoLA()
>>> column = "sentence"
>>> tokenizer = BasicTokenizer()
>>> train_dataset, vocab = CoLA_Process(train_dataset, column, tokenizer)
>>> train_dataset = train_dataset.create_tuple_iterator()
>>> print(next(train_dataset))
[Tensor(shape=[], dtype=String, value= 'gj04'), Tensor(shape=[], dtype=String, value= '1'),
Tensor(shape=[17], dtype=Int32, value= [ 854, 290, 196, 10, 28, 182, 57, 738, 9,
816, 1372, 1, 768, 99, 71, 5316, 0])]
"""
return common_process(dataset, column, tokenizer, vocab)