tfds.folder_dataset.TranslateFolder

Generic text translation dataset created from manual directory.

Inherits From: DatasetBuilder

tfds.folder_dataset.TranslateFolder(
    root_dir: str
)

The directory content should be as followed:

path/to/my_data/
  lang1.train.txt
  lang2.train.txt
  lang1.test.txt
  lang2.test.txt
  ...

Each files should have one example per line. Line order should match between files.

To use it:

builder = tfds.TranslateFolder(root_dir='path/to/my_data/')
print(builder.info)  # Splits, num examples,... are automatically calculated
ds = builder.as_dataset(split='train', shuffle_files=True)

data_dir directory to read/write data. Defaults to the value of the environment variable TFDS_DATA_DIR, if set, otherwise falls back to datasets are stored.
config tfds.core.BuilderConfig or str name, optional configuration for the dataset that affects the data generated on disk. Different builder_configs will have their own subdirectories and versions.
version Optional version at which to load the dataset. An error is raised if specified version cannot be satisfied. Eg: '1.2.3', '1.2.*'. The special value "experimental_latest" will use the highest version, even if not default. This is not recommended unless you know what you are doing, as the version could be broken.

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

as_dataset(
    split: Optional[Tree[splits_lib.SplitArg]] = None,
    *,
    batch_size: tfds.typing.Dim = None,
    shuffle_files: bool = False,
    decoders: Optional[TreeDict[decode.partial_decode.DecoderArg]] = None,
    read_config: Optional[tfds.ReadConfig] = None,
    as_supervised: bool = False
)

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.

download_and_prepare

View source

download_and_prepare(
    **kwargs
)

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.

BUILDER_CONFIGS []
MANUAL_DOWNLOAD_INSTRUCTIONS None
RELEASE_NOTES 

{

}

SUPPORTED_VERSIONS []
VERSION Instance of tfds.core.Version
builder_configs 

{

}

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