mindspore.dataset.EnWik9Dataset
- class mindspore.dataset.EnWik9Dataset(dataset_dir, num_samples=None, num_parallel_workers=None, shuffle=True, num_shards=None, shard_id=None, cache=None)[source]
EnWik9 dataset.
The generated dataset has one column
[text]
with type string.- Parameters
dataset_dir (str) – Path to the root directory that contains the dataset.
num_samples (int, optional) – The number of samples to be included in the dataset. Default:
None
, will include all samples.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. Bool type and Shuffle enum are both supported to pass in. Default:
True
. If shuffle isFalse
, no shuffling will be performed. If shuffle isTrue
, it is equivalent to setting shuffle tomindspore.dataset.Shuffle.GLOBAL
. Set the mode of data shuffling by passing in enumeration variables:Shuffle.GLOBAL
: Shuffle both the files and samples.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.
- Raises
RuntimeError – If dataset_dir does not contain data files.
RuntimeError – If num_shards is specified but shard_id is None.
RuntimeError – If shard_id is specified but num_shards is None.
ValueError – If num_parallel_workers exceeds the max thread numbers.
- Tutorial Examples:
Examples
>>> import mindspore.dataset as ds >>> en_wik9_dataset_dir = "/path/to/en_wik9_dataset" >>> dataset2 = ds.EnWik9Dataset(dataset_dir=en_wik9_dataset_dir, num_samples=2, ... shuffle=True)
About EnWik9 dataset:
The data of EnWik9 is UTF-8 encoded XML consisting primarily of English text. It contains 243,426 article titles, of which 85,560 are #REDIRECT to fix broken links, and the rest are regular articles.
The data is UTF-8 clean. All characters are in the range U’0000 to U’10FFFF with valid encodings of 1 to 4 bytes. The byte values 0xC0, 0xC1, and 0xF5-0xFF never occur. Also, in the Wikipedia dumps, there are no control characters in the range 0x00-0x1F except for 0x09 (tab) and 0x0A (linefeed). Linebreaks occur only on paragraph boundaries, so they always have a semantic purpose.
You can unzip the dataset files into the following directory structure and read by MindSpore’s API.
. └── EnWik9 ├── enwik9
Citation:
@NetworkResource{Hutter_prize, author = {English Wikipedia}, url = "https://cs.fit.edu/~mmahoney/compression/textdata.html", month = {March}, year = {2006} }
Pre-processing Operation
Apply a function in this dataset. |
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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. |
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Create an iterator over the dataset. |
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. |