# 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.
# ============================================================================
"""
STSB 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 untar
URL = "http://ixa2.si.ehu.es/stswiki/images/4/48/Stsbenchmark.tar.gz"
MD5 = "4eb0065aba063ef77873d3a9c8088811"
[文档]class Stsb:
"""
STSB dataset source
"""
def __init__(self, path) -> None:
self.path: str = path
self._index,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(len(lines)-1)
for line in lines:
l = line.split("\t")
self._index.append(int(l[3]))
self._label.append(float(l[4]))
self._sentence1.append(l[5])
self._sentence2.append(l[6])
def __getitem__(self, index):
return self._index[index], self._label[index], self._sentence1[
index], self._sentence2[index]
def __len__(self):
return len(self._sentence1)
[文档]@load.register
def STSB(root: str = DEFAULT_ROOT,
split: Union[Tuple[str], str] = ("train", "dev", "test"),
proxies=None):
r"""
Load the STSB 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 = STSB(root, split)
>>> train_iter = dataset_train.create_tuple_iterator()
>>> print(next(train_iter))
[Tensor(shape=[], dtype=Int64, value= 1), Tensor(shape=[], dtype=Float64,
value= 5), Tensor(shape=[], dtype=String, value= 'A plane is taking off.'),
Tensor(shape=[], dtype=String, value= 'An air plane is taking off.')]
"""
cache_dir = os.path.join(root, "datasets", "STSB")
column_names = ["index", "label", "sentence1", "sentence2"]
path_list = []
datasets_list = []
path, _ = cache_file(None,
url=URL,
cache_dir=cache_dir,
md5sum=MD5,
proxies=proxies)
untar(path, cache_dir)
if isinstance(split, str):
path_list.append(
os.path.join(cache_dir, "stsbenchmark", f"sts-{split}.csv"))
else:
for s in split:
path_list.append(
os.path.join(cache_dir, "stsbenchmark", f"sts-{s}.csv"))
for path in path_list:
datasets_list.append(
GeneratorDataset(source=Stsb(path),
column_names=column_names,
shuffle=False))
if len(path_list) == 1:
return datasets_list[0]
return datasets_list
[文档]@process.register
def STSB_Process(dataset,
column: Union[Tuple[str], str] = ("sentence1", "sentence2"),
tokenizer=BasicTokenizer(),
vocab=None
):
"""
the process of the STSB dataset
Args:
dataset (GeneratorDataset): STSB dataset.
column (Tuple[str]|str): the column or columns needed to be transpormed of the STSB 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 STSB, STSB_Process
>>> dataset_train, dataset_dev, dataset_test = STSB()
>>> dataset_train, vocab = STSB_Process(dataset_train)
>>> dataset_train = dataset_train.create_tuple_iterator()
>>> print(next(dataset_train))
[Tensor(shape=[], dtype=Int64, value= 1), Tensor(shape=[], dtype=Float64,
value= 5), Tensor(shape=[6], dtype=Int32, value= [ 5, 263, 6, 448, 135, 0]),
Tensor(shape=[7], dtype=Int32, value= [329, 242, 263, 6, 448, 135, 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