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tfds.dataset_builders.ConllDatasetBuilder

Base class for CoNLL-like formatted datasets.

Inherits From: GeneratorBasedBuilder, DatasetBuilder

It provides functionalities to ease the processing of CoNLL-like datasets. Users can overwrite _generate_examples to customize the pipeline.

file_format EXPERIMENTAL, may change at any time; Format of the record files in which dataset will be read/written to. If None, defaults to tfrecord.
**kwargs Arguments passed to DatasetBuilder.

builder_config tfds.core.BuilderConfig for this builder.
canonical_version

data_dir

data_path

info tfds.core.DatasetInfo for this builder.
release_notes

supported_versions

version

versions Versions (canonical + availables), in preference order.

Methods

as_dataset

View source

Constructs a tf.data.Dataset.

Callers must pass arguments as keyword arguments.

The output types vary depending on the parameters. Examples:

builder = tfds.builder('imdb_reviews')
builder.download_and_prepare()

# Default parameters: Returns the dict of tf.data.Dataset
ds_all_dict = builder.as_dataset()
assert isinstance(ds_all_dict, dict)
print(ds_all_dict.keys())  # ==> ['test', 'train', 'unsupervised']

assert isinstance(ds_all_dict['test'], tf.data.Dataset)
# Each dataset (test, train, unsup.) consists of dictionaries
# {'label': <tf.Tensor: .. dtype=int64, numpy=1>,
#  'text': <tf.Tensor: .. dtype=string, numpy=b"I've watched the movie ..">}
# {'label': <tf.Tensor: .. dtype=int64, numpy=1>,
#  'text': <tf.Tensor: .. dtype=string, numpy=b'If you love Japanese ..'>}

# With as_supervised: tf.data.Dataset only contains (feature, label) tuples
ds_all_supervised = builder.as_dataset(as_supervised=True)
assert isinstance(ds_all_supervised, dict)
print(ds_all_supervised.keys())  # ==> ['test', 'train', 'unsupervised']

assert isinstance(ds_all_supervised['test'], tf.data.Dataset)
# Each dataset (test, train, unsup.) consists of tuples (text, label)
# (<tf.Tensor: ... dtype=string, numpy=b"I've watched the movie ..">,
#  <tf.Tensor: ... dtype=int64, numpy=1>)
# (<tf.Tensor: ... dtype=string, numpy=b"If you love Japanese ..">,
#  <tf.Tensor: ... dtype=int64, numpy=1>)

# Same as above plus requesting a particular split
ds_test_supervised = builder.as_dataset(as_supervised=True, split='test')
assert isinstance(ds_test_supervised, tf.data.Dataset)
# The dataset consists of tuples (text, label)
# (<tf.Tensor: ... dtype=string, numpy=b"I've watched the movie ..">,
#  <tf.Tensor: ... dtype=int64, numpy=1>)
# (<tf.Tensor: ... dtype=string, numpy=b"If you love Japanese ..">,
#  <tf.Tensor: ... dtype=int64, numpy=1>)

Args
split Which split of the data to load (e.g. 'train', 'test', ['train', 'test'], 'train[80%:]',...). See our split API guide. If None, will return all splits in a Dict[Split, tf.data.Dataset].
batch_size int, batch size. Note that variable-length features will be 0-padded if batch_size is set. Users that want more custom behavior should use batch_size=None and use the tf.data API to construct a custom pipeline. If batch_size == -1, will return feature dictionaries of the whole dataset with tf.Tensors instead of a tf.data.Dataset.
shuffle_files bool, whether to shuffle the input files. Defaults to False.
decoders Nested dict of Decoder objects which allow to customize the decoding. The structure should match the feature structure, but only customized feature keys need to be present. See the guide for more info.
read_config tfds.ReadConfig, Additional options to configure the input pipeline (e.g. seed, num parallel reads,...).
as_supervised bool, if True, the returned tf.data.Dataset will have a 2-tuple structure (input, label) according to builder.info.supervised_keys. If False, the default, the returned tf.data.Dataset will have a dictionary with all the features.

Returns
tf.data.Dataset, or if split=None, dict<key: tfds.Split, value: tfds.data.Dataset>.

If batch_size is -1, will return feature dictionaries containing the entire dataset in tf.Tensors instead of a tf.data.Dataset.

create_dataset_info

View source

Initializes dataset_info.DatasetInfo for Conll datasets.

Args
description A short, markdown-formatted description of the dataset.
supervised_keys Specifies the input structure for supervised learning, if applicable for the dataset, used with "as_supervised". Typically this is a (input_key, target_key) tuple.
homepage The homepage of the dataset, if applicable for this dataset.
citation The citation to use for this dataset, if applicable for this dataset.

Returns
dataset_info.DatasetInfo for Conll datasets, populated with the values from the provided arguments.

download_and_prepare

View source

Downloads and prepares dataset for reading.

Args
download_dir str, directory where downloaded files are stored. Defaults to "~/tensorflow-datasets/downloads".
download_config tfds.download.DownloadConfig, further configuration for downloading and preparing dataset.
file_format optional str or file_adapters.FileFormat, format of the record files in which the dataset will be written.

Raises
IOError if there is not enough disk space available.
RuntimeError when the config cannot be found.

get_default_builder_config

View source

Returns the default builder config if there is one.

Note that for dataset builders that cannot use the cls.BUILDER_CONFIGS, we need a method that uses the instance to get BUILDER_CONFIGS and DEFAULT_BUILDER_CONFIG_NAME.

Returns
the default builder config if there is one

_info

View source

Returns the tfds.core.DatasetInfo object.

This function is called once and the result is cached for all following calls.

Returns
dataset_info The dataset metadata.

BUILDER_CONFIGS []
DEFAULT_BUILDER_CONFIG_NAME None
MANUAL_DOWNLOAD_INSTRUCTIONS None
RELEASE_NOTES

{

}

SUPPORTED_VERSIONS []
VERSION None
builder_config_cls None
builder_configs

{

}

code_path Instance of etils.epath.gpath.PosixGPath
default_builder_config None
name 'conll_dataset_builder'
url_infos None