Differences with torchtext.datasets.CoNLL2000Chunking

torchtext.datasets.CoNLL2000Chunking

class torchtext.datasets.CoNLL2000Chunking(
    root: str = '.data',
    split: Union[List[str], str] = ('train', 'test'))

For more information, see torchtext.datasets.CoNLL2000Chunking.

mindspore.dataset.CoNLL2000Dataset

class mindspore.dataset.CoNLL2000Dataset(
    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.CoNLL2000Dataset.

Differences

PyTorch: Read the CoNLL2000 dataset.

MindSpore: Read the CoNLL2000 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.CoNLL2000Chunking(root, split=('train', 'test'))
dataloader = DataLoader(dataset)

# MindSpore
import mindspore.dataset as ds

# Download CoNLL2000 dataset files, unzip into the following structure
# .
# └── /path/to/dataset_directory/
#      ├── train.txt
#      ├── test.txt
#      ├── readme.txt
root = "/path/to/dataset_directory/"
ms_dataloader = ds.CoNLL2000Dataset(root, usage='all')