mindspore.dataset.CLUEDataset

class mindspore.dataset.CLUEDataset(dataset_files, task='AFQMC', usage='train', num_samples=None, num_parallel_workers=None, shuffle=Shuffle.GLOBAL, num_shards=None, shard_id=None, cache=None)

CLUE(Chinese Language Understanding Evaluation) dataset. Supported CLUE classification tasks: 'AFQMC' , 'TNEWS', 'IFLYTEK', 'CMNLI', 'WSC' and 'CSL'.

Parameters

• dataset_files (Union[str, list[str]]) – String or list of files to be read or glob strings to search for a pattern of files. The list will be sorted in a lexicographical order.

• task (str, optional) – The kind of task, one of 'AFQMC' , 'TNEWS', 'IFLYTEK', 'CMNLI', 'WSC' and 'CSL'. Default: 'AFQMC' .

• usage (str, optional) – Specify the 'train', 'test' or 'eval' part of dataset. Default: 'train'.

• num_samples (int, optional) – The number of samples to be included in the dataset. Default: None , will include all images.

• num_parallel_workers (int, optional) – Number of worker threads to read the data. Default: None , will use global default workers(8), it can be set by mindspore.dataset.config.set_num_parallel_workers() .

• shuffle (Union[bool, Shuffle], optional) –

  Perform reshuffling of the data every epoch. Default: Shuffle.GLOBAL . Bool type and Shuffle enum are both supported to pass in. If shuffle is False, no shuffling will be performed. If shuffle is True, performs global shuffle. There are three levels of shuffling, desired shuffle enum defined by mindspore.dataset.Shuffle .

  • Shuffle.GLOBAL : Shuffle both the files and samples, same as setting shuffle to True.

  • Shuffle.FILES : Shuffle files only.

• num_shards (int, optional) – Number of shards that the dataset will be divided into. Default: None . When this argument is specified, num_samples reflects the maximum sample number of per shard. Used in data parallel training .

• shard_id (int, optional) – The shard ID within num_shards . Default: None . This argument can only be specified when num_shards is also specified.

• cache (DatasetCache, optional) – Use tensor caching service to speed up dataset processing. More details: Single-Node Data Cache . Default: None , which means no cache is used.

The generated dataset with different task setting has different output columns:

task	usage	Output column
AFQMC	train	[sentence1, dtype=string] [sentence2, dtype=string] [label, dtype=string]
	test	[id, dtype=uint32] [sentence1, dtype=string] [sentence2, dtype=string]
	eval	[sentence1, dtype=string] [sentence2, dtype=string] [label, dtype=string]
TNEWS	train	[label, dtype=string] [label_des, dtype=string] [sentence, dtype=string] [keywords, dtype=string]
	test	[label, dtype=uint32] [keywords, dtype=string] [sentence, dtype=string]
	eval	[label, dtype=string] [label_des, dtype=string] [sentence, dtype=string] [keywords, dtype=string]
IFLYTEK	train	[label, dtype=string] [label_des, dtype=string] [sentence, dtype=string]
	test	[id, dtype=uint32] [sentence, dtype=string]
	eval	[label, dtype=string] [label_des, dtype=string] [sentence, dtype=string]
CMNLI	train	[sentence1, dtype=string] [sentence2, dtype=string] [label, dtype=string]
	test	[id, dtype=uint32] [sentence1, dtype=string] [sentence2, dtype=string]
	eval	[sentence1, dtype=string] [sentence2, dtype=string] [label, dtype=string]
WSC	train	[span1_index, dtype=uint32] [span2_index, dtype=uint32] [span1_text, dtype=string] [span2_text, dtype=string] [idx, dtype=uint32] [text, dtype=string] [label, dtype=string]
	test	[span1_index, dtype=uint32] [span2_index, dtype=uint32] [span1_text, dtype=string] [span2_text, dtype=string] [idx, dtype=uint32] [text, dtype=string]
	eval	[span1_index, dtype=uint32] [span2_index, dtype=uint32] [span1_text, dtype=string] [span2_text, dtype=string] [idx, dtype=uint32] [text, dtype=string] [label, dtype=string]
CSL	train	[id, dtype=uint32] [abst, dtype=string] [keyword, dtype=string] [label, dtype=string]
	test	[id, dtype=uint32] [abst, dtype=string] [keyword, dtype=string]
	eval	[id, dtype=uint32] [abst, dtype=string] [keyword, dtype=string] [label, dtype=string]

Raises

• ValueError – If dataset_files are not valid or do not exist.

• ValueError – task is not in 'AFQMC' , 'TNEWS', 'IFLYTEK', 'CMNLI', 'WSC' or 'CSL'.

• ValueError – usage is not in 'train', 'test' or 'eval'.

• ValueError – If num_parallel_workers exceeds the max thread numbers.

• ValueError – If shard_id is not in range of [0, num_shards ).

• RuntimeError – If num_shards is specified but shard_id is None.

• RuntimeError – If shard_id is specified but num_shards is None.

Tutorial Examples:

• Load & Process Data With Dataset Pipeline

Examples

>>> import mindspore.dataset as ds
>>> clue_dataset_dir = ["/path/to/clue_dataset_file"] # contains 1 or multiple clue files
>>> dataset = ds.CLUEDataset(dataset_files=clue_dataset_dir, task='AFQMC', usage='train')

About CLUE dataset:

CLUE, a Chinese Language Understanding Evaluation benchmark. It contains multiple tasks, including single-sentence classification, sentence pair classification, and machine reading comprehension.

You can unzip the dataset files into the following structure and read by MindSpore's API, such as afqmc dataset:

.
└── afqmc_public
     ├── train.json
     ├── test.json
     └── dev.json

Citation:

@article{CLUEbenchmark,
title   = {CLUE: A Chinese Language Understanding Evaluation Benchmark},
author  = {Liang Xu, Xuanwei Zhang, Lu Li, Hai Hu, Chenjie Cao, Weitang Liu, Junyi Li, Yudong Li,
        Kai Sun, Yechen Xu, Yiming Cui, Cong Yu, Qianqian Dong, Yin Tian, Dian Yu, Bo Shi, Jun Zeng,
        Rongzhao Wang, Weijian Xie, Yanting Li, Yina Patterson, Zuoyu Tian, Yiwen Zhang, He Zhou,
        Shaoweihua Liu, Qipeng Zhao, Cong Yue, Xinrui Zhang, Zhengliang Yang, Zhenzhong Lan},
journal = {arXiv preprint arXiv:2004.05986},
year    = {2020},
howpublished = {https://github.com/CLUEbenchmark/CLUE}
}

Pre-processing Operation

mindspore.dataset.Dataset.apply	Apply a function in this dataset.
mindspore.dataset.Dataset.concat	Concatenate the dataset objects in the input list.
mindspore.dataset.Dataset.filter	Filter dataset by prediction.
mindspore.dataset.Dataset.flat_map	Map func to each row in dataset and flatten the result.
mindspore.dataset.Dataset.map	Apply each operation in operations to this dataset.
mindspore.dataset.Dataset.project	The specified columns will be selected from the dataset and passed into the pipeline with the order specified.
mindspore.dataset.Dataset.rename	Rename the columns in input datasets.
mindspore.dataset.Dataset.repeat	Repeat this dataset count times.
mindspore.dataset.Dataset.reset	Reset the dataset for next epoch.
mindspore.dataset.Dataset.save	Save the dynamic data processed by the dataset pipeline in common dataset format.
mindspore.dataset.Dataset.shuffle	Shuffle the dataset by creating a cache with the size of buffer_size .
mindspore.dataset.Dataset.skip	Skip the first N elements of this dataset.
mindspore.dataset.Dataset.split	Split the dataset into smaller, non-overlapping datasets.
mindspore.dataset.Dataset.take	Take the first specified number of samples from the dataset.
mindspore.dataset.Dataset.zip	Zip the datasets in the sense of input tuple of datasets.

Batch

mindspore.dataset.Dataset.batch	Combine batch_size number of consecutive rows into batch which apply per_batch_map to the samples first.
mindspore.dataset.Dataset.bucket_batch_by_length	Bucket elements according to their lengths.
mindspore.dataset.Dataset.padded_batch	Combine batch_size number of consecutive rows into batch which apply pad_info to the samples first.

Iterator

mindspore.dataset.Dataset.create_dict_iterator	Create an iterator over the dataset that yields samples of type dict, while the key is the column name and the value is the data.
mindspore.dataset.Dataset.create_tuple_iterator	Create an iterator over the dataset that yields samples of type list, whose elements are the data for each column.

Attribute

mindspore.dataset.Dataset.get_batch_size	Return the size of batch.
mindspore.dataset.Dataset.get_class_indexing	Get the mapping dictionary from category names to category indexes.
mindspore.dataset.Dataset.get_col_names	Return the names of the columns in dataset.
mindspore.dataset.Dataset.get_dataset_size	Return the number of batches in an epoch.
mindspore.dataset.Dataset.get_repeat_count	Get the replication times in RepeatDataset.
mindspore.dataset.Dataset.input_indexs	Get the column index, which represents the corresponding relationship between the data column order and the network when using the sink mode.
mindspore.dataset.Dataset.num_classes	Get the number of classes in a dataset.
mindspore.dataset.Dataset.output_shapes	Get the shapes of output data.
mindspore.dataset.Dataset.output_types	Get the types of output data.

Apply Sampler

mindspore.dataset.MappableDataset.add_sampler	Add a child sampler for the current dataset.
mindspore.dataset.MappableDataset.use_sampler	Replace the last child sampler of the current dataset, remaining the parent sampler unchanged.

Others

mindspore.dataset.Dataset.sync_update	Release a blocking condition and trigger callback with given data.
mindspore.dataset.Dataset.sync_wait	Add a blocking condition to the input Dataset and a synchronize action will be applied.
mindspore.dataset.Dataset.to_json	Serialize a pipeline into JSON string and dump into file if filename is provided.