# 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.
# ============================================================================
"""
QNLI 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
from mindnlp.utils import unzip
URL = "https://dl.fbaipublicfiles.com/glue/data/QNLIv2.zip"
MD5 = "b4efd6554440de1712e9b54e14760e82"
[文档]class Qnli:
"""
QNLI dataset source
"""
label_map = {
"not_entailment": 1,
"entailment": 0
}
def __init__(self, path) -> None:
self.path: str = path
self._label, self._question, self._sentence = [], [], []
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._question.append(l[1])
self._sentence.append(l[2])
if not self.path.endswith("test.tsv"):
self._label.append(self.label_map[l[3]])
def __getitem__(self, index):
if not self.path.endswith("test.tsv"):
return self._label[index], self._question[index], self._sentence[index]
return self._question[index], self._sentence[index]
def __len__(self):
return len(self._sentence)
[文档]@load.register
def QNLI(
root: str = DEFAULT_ROOT, split: Union[Tuple[str], str] = ("train", "dev", "test"), proxies=None
):
r"""
Load the QNLI 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 = QNLI(root, split)
>>> train_iter = dataset_train.create_tuple_iterator()
>>> print(next(train_iter))
"""
cache_dir = os.path.join(root, "datasets", "QNLI")
path_dict = {
"train": "train.tsv",
"dev": "dev.tsv",
"test": "test.tsv",
}
column_names = {
"train": ["label", "question", "sentence"],
"dev": ["label", "question", "sentence"],
"test": ["question", "sentence"],
}
path_list = []
column_names_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, "QNLI", path_dict[split])
)
column_names_list.append(column_names[split])
else:
for s in split:
path_list.append(
os.path.join(cache_dir, "QNLI", path_dict[s])
)
column_names_list.append(column_names[s])
for idx, path in enumerate(path_list):
datasets_list.append(
GeneratorDataset(
source=Qnli(path), column_names=column_names_list[idx], shuffle=False
)
)
if len(path_list) == 1:
return datasets_list[0]
return datasets_list
[文档]@process.register
def QNLI_Process(dataset,
column: Union[Tuple[str], str] = ("question", "sentence"),
tokenizer=BasicTokenizer(),
vocab=None
):
"""
the process of the QNLI dataset
Args:
dataset (GeneratorDataset): QNLI dataset.
column (Tuple[str]|str): the column or columns needed to be transpormed of the QNLI 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 QNLI, QNLI_Process
>>> dataset_train, dataset_dev, dataset_test = QNLI()
>>> dataset_train, vocab = QNLI_Process(dataset_train)
>>> dataset_train = dataset_train.create_tuple_iterator()
>>> print(next(dataset_train))
[Tensor(shape=[], dtype=Int64, value= 1), Tensor(shape=[8],
dtype=Int32, value= [ 49, 25, 0, 382, 2323, 574, 380, 4]),
Tensor(shape=[45], dtype=Int32, value= [ 3377, 0, 65, 1913, 180, 36,
5, 53, 2, 0, 1913, 19, 662, 1, 2323, 26903, 1857, 8, 8531,
5, 63, 9937, 1420, 7, 45, 1325, 3042, 2323, 58, 77, 44,
76653, 70, 46, 3140, 5, 63, 1164, 793, 272, 6, 0, 389, 486, 3])]
"""
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