mindspore.dataset.SQuADDataset

class mindspore.dataset.SQuADDataset(dataset_dir, usage=None, num_samples=None, num_parallel_workers=None, shuffle=Shuffle.GLOBAL, num_shards=None, shard_id=None, cache=None)[source]

SQuAD 1.1 and SQuAD 2.0 datasets.

The generated dataset with different versions and usages has the same output columns: [context, question, text, answer_start] . The tensor of column context is of the string type. The tensor of column question is of the string type. The tensor of column text is the answer in the context of the string type. The tensor of column answer_start is the start index of answer in context, which is of the uint32 type.

Parameters

dataset_dir (str) – Path to the root directory that contains the dataset.
usage (str, optional) – Specify the 'train', 'dev' or 'all' part of dataset. Default: None , all samples.
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 by mindspore.dataset.config.set_num_parallel_workers() .
shuffle (Union[bool, Shuffle], optional) –
Whether to shuffle the dataset. Default: Shuffle.GLOBAL . If False is provided, no shuffling will be performed. If True is provided, it is the same as setting to mindspore.dataset.Shuffle.GLOBAL . If Shuffle is provided, the effect is as follows:
- 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. 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.

Raises

RuntimeError – If dataset_dir does not contain data files.
ValueError – If num_parallel_workers exceeds the max thread numbers.
RuntimeError – If num_shards is specified but shard_id is None.
RuntimeError – If shard_id is specified but num_shards is None.
ValueError – If shard_id is not in range of [0, num_shards ).

Tutorial Examples:

Load & Process Data With Dataset Pipeline

Examples

>>> import mindspore.dataset as ds
>>> squad_dataset_dir = "/path/to/squad_dataset_file"
>>> dataset = ds.SQuADDataset(dataset_dir=squad_dataset_dir, usage='all')

About SQuAD dataset:

SQuAD (Stanford Question Answering Dataset) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.

SQuAD 1.1, the previous version of the SQuAD dataset, contains 100,000+ question-answer pairs on 500+ articles. SQuAD 2.0 combines the 100,000 questions in SQuAD 1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers to look similar to answerable ones. To do well on SQuAD 2.0, systems must not only answer questions when possible, but also determine when no answer is supported by the paragraph and abstain from answering.

You can get the dataset files into the following structure and read by MindSpore's API,

For SQuAD 1.1:

.
└── SQuAD1
     ├── train-v1.1.json
     └── dev-v1.1.json

For SQuAD 2.0:

.
└── SQuAD2
     ├── train-v2.0.json
     └── dev-v2.0.json

Citation:

@misc{rajpurkar2016squad,
    title         = {SQuAD: 100,000+ Questions for Machine Comprehension of Text},
    author        = {Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
    year          = {2016},
    eprint        = {1606.05250},
    archivePrefix = {arXiv},
    primaryClass  = {cs.CL}
}

@misc{rajpurkar2018know,
    title         = {Know What You Don't Know: Unanswerable Questions for SQuAD},
    author        = {Pranav Rajpurkar and Robin Jia and Percy Liang},
    year          = {2018},
    eprint        = {1806.03822},
    archivePrefix = {arXiv},
    primaryClass  = {cs.CL}
}

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.