# 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.
# ============================================================================
"""
SST2 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://dl.fbaipublicfiles.com/glue/data/SST-2.zip"
MD5 = "9f81648d4199384278b86e315dac217c"
[docs]class Sst2:
"""
SST2 dataset source
"""
def __init__(self, path) -> None:
self.path: str = path
self._label, self._text = [], []
self._load()
def _load(self):
with open(self.path, "r", encoding="utf-8") as f:
dataset = f.read()
lines = dataset.split("\n")
lines.pop(0)
lines.pop(len(lines) - 1)
if self.path.endswith("test.tsv"):
for line in lines:
l = line.split("\t")
self._text.append(l[1])
else:
for line in lines:
l = line.split("\t")
self._text.append(l[0])
self._label.append(l[1])
def __getitem__(self, index):
if self.path.endswith("test.tsv"):
return self._text[index]
return self._label[index], self._text[index]
def __len__(self):
return len(self._text)
[docs]@load.register
def SST2(
root: str = DEFAULT_ROOT, split: Union[Tuple[str], str] = ("train", "dev", "test"), proxies=None
):
r"""
Load the SST2 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 = SST2(root, split)
>>> train_iter = dataset_train.create_tuple_iterator()
>>> print(next(train_iter))
[Tensor(shape=[], dtype=String, value= '0'), Tensor(shape=[], dtype=String, \
value= 'hide new secretions from the parental units ')]
"""
cache_dir = os.path.join(root, "datasets", "SST2")
column_names = []
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, "SST-2", split + ".tsv"))
if split == "test":
column_names.append(["text"])
else:
column_names.append(["label", "text"])
else:
for s in split:
path_list.append(os.path.join(cache_dir, "SST-2", s + ".tsv"))
if split == "test":
column_names.append(["text"])
else:
column_names.append(["label", "text"])
for idx, path in enumerate(path_list):
datasets_list.append(
GeneratorDataset(
source=Sst2(path), column_names=column_names[idx], shuffle=False
)
)
if len(path_list) == 1:
return datasets_list[0]
return datasets_list
[docs]@process.register
def SST2_Process(dataset, column="text", tokenizer=BasicTokenizer(), vocab=None):
"""
the process of the SST2 dataset
Args:
dataset (GeneratorDataset): SST2 dataset.
column (str): the column needed to be transpormed of the sst2 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 SST2, SST2_Process
>>> train_dataset, dataset_dev, test_dataset = SST2()
>>> column = "text"
>>> tokenizer = BasicTokenizer()
>>> train_dataset, vocab = SST2_Process(train_dataset, column, tokenizer)
>>> train_dataset = train_dataset.create_tuple_iterator()
>>> print(next(train_dataset))
{'label': Tensor(shape=[], dtype=String, value= '0'), 'text': Tensor(shape=[7],
dtype=Int32, value= [ 4699, 92, 12483, 36, 0, 7598, 9597])}
"""
return common_process(dataset, column, tokenizer, vocab)