mindspore.dataset.IWSLT2016Dataset

class mindspore.dataset.IWSLT2016Dataset(dataset_dir, usage=None, language_pair=None, valid_set=None, test_set=None, num_samples=None, shuffle=Shuffle.GLOBAL, num_shards=None, shard_id=None, num_parallel_workers=None, cache=None)

IWSLT2016(International Workshop on Spoken Language Translation) dataset.

The generated dataset has two columns: [text, translation] . The tensor of column :py:obj: text is of the string type. The column :py:obj: translation is of the string type.

Parameters

• dataset_dir (str) – Path to the root directory that contains the dataset.

• usage (str, optional) – Acceptable usages include 'train', 'valid', 'test' and 'all'. Default: None , all samples.

• language_pair (sequence, optional) – Sequence containing source and target language, supported values are ('en', 'fr'), ('en', 'de'), ('en', 'cs'), ('en', 'ar'), ('fr', 'en'), ('de', 'en'), ('cs', 'en'), ('ar', 'en'). Default: None, set to ('de', 'en').

• valid_set (str, optional) – A string to identify validation set, when usage is valid or all, the validation set of valid_set type will be read, supported values are 'dev2010', 'tst2010', 'tst2011', 'tst2012', 'tst2013' and 'tst2014'. Default: None, set to 'tst2013'.

• test_set (str, optional) – A string to identify test set, when usage is test or all, the test set of test_set type will be read, supported values are 'dev2010', 'tst2010', 'tst2011', 'tst2012', 'tst2013' and 'tst2014'. Default: None, set to 'tst2014'.

• num_samples (int, optional) – Number of samples (rows) to read. Default: None , reads the full dataset.

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

  Perform reshuffling of the data every epoch. Bool type and Shuffle enum are both supported to pass in. Default: Shuffle.GLOBAL . If shuffle is False, no shuffling will be performed. If shuffle is True, it is equivalent to setting shuffle to mindspore.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 max 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.

• 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() .

• 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:

• Load & Process Data With Dataset Pipeline

Examples

>>> import mindspore.dataset as ds
>>> iwslt2016_dataset_dir = "/path/to/iwslt2016_dataset_dir"
>>> dataset = ds.IWSLT2016Dataset(dataset_dir=iwslt2016_dataset_dir, usage='all',
...                               language_pair=('de', 'en'), valid_set='tst2013', test_set='tst2014')

About IWSLT2016 dataset:

IWSLT is an international oral translation conference, a major annual scientific conference dedicated to all aspects of oral translation. The MT task of the IWSLT evaluation activity constitutes a dataset, which can be publicly obtained through the WIT3 website wit3 . The IWSLT2016 dataset includes translations from English to Arabic, Czech, French, and German, and translations from Arabic, Czech, French, and German to English.

You can unzip the original IWSLT2016 dataset files into this directory structure and read by MindSpore's API. After decompression, you also need to decompress the dataset to be read in the specified folder. For example, if you want to read the dataset of de-en, you need to unzip the tgz file in the de/en directory, the dataset is in the unzipped folder.

.
└── iwslt2016_dataset_directory
     ├── subeval_files
     └── texts
          ├── ar
          │    └── en
          │        └── ar-en
          ├── cs
          │    └── en
          │        └── cs-en
          ├── de
          │    └── en
          │        └── de-en
          │            ├── IWSLT16.TED.dev2010.de-en.de.xml
          │            ├── train.tags.de-en.de
          │            ├── ...
          ├── en
          │    ├── ar
          │    │   └── en-ar
          │    ├── cs
          │    │   └── en-cs
          │    ├── de
          │    │   └── en-de
          │    └── fr
          │        └── en-fr
          └── fr
               └── en
                   └── fr-en

Citation:

@inproceedings{cettoloEtAl:EAMT2012,
Address = {Trento, Italy},
Author = {Mauro Cettolo and Christian Girardi and Marcello Federico},
Booktitle = {Proceedings of the 16$^{th}$ Conference of the European Association for Machine Translation
             (EAMT)},
Date = {28-30},
Month = {May},
Pages = {261--268},
Title = {WIT$^3$: Web Inventory of Transcribed and Translated Talks},
Year = {2012}}

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.