# 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.
# ============================================================================
"""
MRPC load function
"""
# pylint: disable=C0103
import os
from typing import Union, Tuple
from mindspore.dataset import GeneratorDataset, text
from mindnlp.utils.download import cache_file
from mindnlp.dataset.process import common_process
from mindnlp.dataset.register import load, process
from mindnlp.dataset.transforms import BasicTokenizer
from mindnlp.configs import DEFAULT_ROOT
URL = {
"train": "https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt",
"test": "https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt",
}
MD5 = {
"train": "793daf7b6224281e75fe61c1f80afe35",
"test": "e437fdddb92535b820fe8852e2df8a49",
}
[docs]class Mrpc:
"""
MRPC dataset source
"""
def __init__(self, path):
self.path = path
self._label, self._sentence1, self._sentence2 = [], [], []
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)
for line in lines:
l = line.split("\t")
self._label.append(int(l[0]))
self._sentence1.append(l[3])
self._sentence2.append(l[4])
def __getitem__(self, index):
return self._label[index], self._sentence1[index], self._sentence2[index]
def __len__(self):
return len(self._label)
[docs]@load.register
def MRPC(root: str = DEFAULT_ROOT, split: Union[Tuple[str], str] = ("train", "test"), proxies=None):
r"""
Load the MRPC dataset
Args:
root (str): Directory where the datasets are saved.
Default:~/.mindnlp
split (str|Tuple[str]): Split or splits to be returned.
Default:('train', '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", "test")
>>> dataset_train,dataset_test = MRPC(root, split)
>>> train_iter = dataset_train.create_tuple_iterator()
>>> print(next(train_iter))
"""
cache_dir = os.path.join(root, "datasets", "MRPC")
column_names = ["label", "sentence1", "sentence2"]
datasets_list = []
path_list = []
if isinstance(split,str):
path, _ = cache_file(None, url=URL[split], cache_dir=cache_dir, md5sum=MD5[split], proxies=proxies)
path_list.append(path)
else:
for s in split:
path, _ = cache_file(None, url=URL[s], cache_dir=cache_dir, md5sum=MD5[s], proxies=proxies)
path_list.append(path)
for path in path_list:
datasets_list.append(GeneratorDataset(source=Mrpc(path), column_names=column_names, shuffle=False))
if len(path_list) == 1:
return datasets_list[0]
return datasets_list
[docs]@process.register
def MRPC_Process(dataset,
column: Union[Tuple[str], str] = ("sentence1", "sentence2"),
tokenizer=BasicTokenizer(),
vocab=None
):
"""
the process of the MRPC dataset
Args:
dataset (GeneratorDataset): MRPC dataset.
column (Tuple[str]|str): the column or columns needed to be transpormed of the MRPC 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 `column` is not a string or Tuple[str]
Examples:
>>> from mindnlp.dataset import MRPC, MRPC_Process
>>> dataset_train, dataset_test = MRPC()
>>> dataset_train, vocab = MRPC_Process(dataset_train)
>>> dataset_train = dataset_train.create_tuple_iterator()
>>> print(next(dataset_train))
[Tensor(shape=[], dtype=Int64, value= 1), Tensor(shape=[19],
dtype=Int32, value= [1555, 527, 36, 1838, 1, 1547, 33, 226, 8, 2, 1156,
8, 1, 4, 4932, 9179, 36, 362, 0]), Tensor(shape=[20], dtype=Int32,
value= [5820, 3, 151, 27, 119, 8, 2, 1156, 8, 1, 1555, 527, 36, 1838,
4, 4932, 9179, 36, 362, 0])]
"""
if isinstance(column, str):
return common_process(dataset, column, tokenizer, vocab)
if vocab is None:
for col in column:
dataset = dataset.map(tokenizer, input_columns=col)
column = list(column)
vocab = text.Vocab.from_dataset(dataset, columns=column, special_tokens=["<pad>", "<unk>"])
for col in column:
dataset = dataset.map(text.Lookup(vocab, unknown_token='<unk>'), input_columns=col)
return dataset, vocab
for col in column:
dataset = dataset.map(tokenizer, input_columns=col)
for col in column:
dataset = dataset.map(text.Lookup(vocab, unknown_token='<unk>'), input_columns=col)
return dataset, vocab