Differences with torchtext.datasets.SQuAD2
torchtext.datasets.SQuAD2
class torchtext.datasets.SQuAD2(
root: str = '.data',
split: Union[List[str], str] = ('train', 'dev'))
For more information, see torchtext.datasets.SQuAD2.
mindspore.dataset.SQuADDataset
class mindspore.dataset.SQuADDataset(
dataset_dir,
usage=None,
num_samples=None,
num_parallel_workers=None,
shuffle=Shuffle.GLOBAL,
num_shards=None,
shard_id=None,
cache=None)
For more information, see mindspore.dataset.SQuADDataset.
Differences
PyTorch: Read the SQuAD2 dataset.
MindSpore: Read the SQuAD2 dataset. Downloading dataset from web is not supported.
Categories |
Subcategories |
PyTorch |
MindSpore |
Difference |
---|---|---|---|---|
Parameter |
Parameter1 |
root |
dataset_dir |
- |
Parameter2 |
split |
usage |
- |
|
Parameter3 |
- |
num_samples |
The number of images to be included in the dataset |
|
Parameter4 |
- |
num_parallel_workers |
Number of worker threads to read the data |
|
Parameter5 |
- |
shuffle |
Whether to perform shuffle on the dataset |
|
Parameter6 |
- |
num_shards |
Number of shards that the dataset will be divided into |
|
Parameter7 |
- |
shard_id |
The shard ID within num_shards |
|
Parameter8 |
- |
cache |
Use tensor caching service to speed up dataset processing |
Code Example
# PyTorch
import torchtext.datasets as datasets
from torch.utils.data import DataLoader
root = "/path/to/dataset_directory/"
dataset = datasets.SQuAD2(root, split=('train', 'dev'))
dataloader = DataLoader(dataset)
# MindSpore
import mindspore.dataset as ds
# Download SQuAD2 dataset files, unzip into the following structure
# .
# └── /path/to/dataset_directory/
# ├── train-v2.0.json
# ├── dev-v2.0.json
root = "/path/to/dataset_directory/"
ms_dataloader = ds.SQuADDataset(root, usage='all')