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)[source]
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 bymindspore.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 isFalse
, no shuffling will be performed. If shuffle isTrue
, performs global shuffle. There are three levels of shuffling, desired shuffle enum defined bymindspore.dataset.Shuffle
.Shuffle.GLOBAL
: Shuffle both the files and samples, same as setting shuffle toTrue
.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.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:
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
Apply a function in this dataset. |
|
Concatenate the dataset objects in the input list. |
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Filter dataset by prediction. |
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Map func to each row in dataset and flatten the result. |
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Apply each operation in operations to this dataset. |
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The specified columns will be selected from the dataset and passed into the pipeline with the order specified. |
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Rename the columns in input datasets. |
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Repeat this dataset count times. |
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Reset the dataset for next epoch. |
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Save the dynamic data processed by the dataset pipeline in common dataset format. |
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Shuffle the dataset by creating a cache with the size of buffer_size . |
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Skip the first N elements of this dataset. |
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Split the dataset into smaller, non-overlapping datasets. |
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Take the first specified number of samples from the dataset. |
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Zip the datasets in the sense of input tuple of datasets. |
Batch
Combine batch_size number of consecutive rows into batch which apply per_batch_map to the samples first. |
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Bucket elements according to their lengths. |
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Combine batch_size number of consecutive rows into batch which apply pad_info to the samples first. |
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. |
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Create an iterator over the dataset that yields samples of type list, whose elements are the data for each column. |
Attribute
Return the size of batch. |
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Get the mapping dictionary from category names to category indexes. |
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Return the names of the columns in dataset. |
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Return the number of batches in an epoch. |
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Get the replication times in RepeatDataset. |
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Get the column index, which represents the corresponding relationship between the data column order and the network when using the sink mode. |
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Get the number of classes in a dataset. |
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Get the shapes of output data. |
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Get the types of output data. |
Apply Sampler
Add a child sampler for the current dataset. |
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Replace the last child sampler of the current dataset, remaining the parent sampler unchanged. |
Others
Release a blocking condition and trigger callback with given data. |
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Add a blocking condition to the input Dataset and a synchronize action will be applied. |
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Serialize a pipeline into JSON string and dump into file if filename is provided. |