mindspore.dataset.CoNLL2000Dataset
- class mindspore.dataset.CoNLL2000Dataset(dataset_dir, usage=None, num_samples=None, shuffle=Shuffle.GLOBAL, num_shards=None, shard_id=None, num_parallel_workers=None, cache=None)[source]
CoNLL-2000(Conference on Computational Natural Language Learning) chunking dataset.
The generated dataset has three columns:
[word, pos_tag, chunk_tag]. The tensors of columnword, columnpos_tag, and columnchunk_tagare of the string type.- Parameters
dataset_dir (str) – Path to the root directory that contains the CoNLL2000 chunking dataset.
usage (str, optional) – Usage of dataset, can be
'train','test', or'all'. For dataset,'train'will read from 8,936 train samples,'test'will read from 2,012 test samples,'all'will read from all 1,0948 samples. Default:None, read all samples.num_samples (int, optional) – Number of samples (rows) to be read. Default:
None, read the full dataset.shuffle (Union[bool, Shuffle], optional) –
Perform reshuffling of the data every epoch. Default:
Shuffle.GLOBAL. 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. When this argument is specified, num_samples reflects the max sample number of per shard. Default:
None. Used in data parallel training .shard_id (int, optional) – The shard ID within num_shards . This argument can only be specified when num_shards is also specified. Default:
None.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().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.
- Tutorial Examples:
Examples
>>> import mindspore.dataset as ds >>> conll2000_dataset_dir = "/path/to/conll2000_dataset_dir" >>> dataset = ds.CoNLL2000Dataset(dataset_dir=conll2000_dataset_dir, usage='all')
About CoNLL2000 Dataset:
The CoNLL2000 chunking dataset consists of the text from sections 15-20 of the Wall Street Journal corpus. Texts are chunked using IOB notation, and the chunk type has NP, VP, PP, ADJP and ADVP. The dataset consist of three columns separated by spaces. The first column contains the current word, the second is part-of-speech tag as derived by the Brill tagger and the third is chunk tag as derived from the WSJ corpus. Text chunking consists of dividing a text in syntactically correlated parts of words.
You can unzip the dataset files into the following structure and read by MindSpore's API:
. └── conll2000_dataset_dir ├── train.txt ├── test.txt └── readme.txtCitation:
@inproceedings{tksbuchholz2000conll, author = {Tjong Kim Sang, Erik F. and Sabine Buchholz}, title = {Introduction to the CoNLL-2000 Shared Task: Chunking}, editor = {Claire Cardie and Walter Daelemans and Claire Nedellec and Tjong Kim Sang, Erik}, booktitle = {Proceedings of CoNLL-2000 and LLL-2000}, publisher = {Lisbon, Portugal}, pages = {127--132}, year = {2000} }
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 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. |