Datasets:

leosltl
/

Android-in-the-Wild

Error code:   StreamingRowsError
Exception:    ArrowInvalid
Message:      JSON parse error: Invalid value. in row 0
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 276, in _generate_tables
                  df = pandas_read_json(f)
                       ^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 34, in pandas_read_json
                  return pd.read_json(path_or_buf, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/pandas/io/json/_json.py", line 815, in read_json
                  return json_reader.read()
                         ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/pandas/io/json/_json.py", line 1014, in read
                  obj = self._get_object_parser(self.data)
                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/pandas/io/json/_json.py", line 1040, in _get_object_parser
                  obj = FrameParser(json, **kwargs).parse()
                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/pandas/io/json/_json.py", line 1176, in parse
                  self._parse()
                File "/usr/local/lib/python3.12/site-packages/pandas/io/json/_json.py", line 1391, in _parse
                  self.obj = DataFrame(
                             ^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/pandas/core/frame.py", line 778, in __init__
                  mgr = dict_to_mgr(data, index, columns, dtype=dtype, copy=copy, typ=manager)
                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/pandas/core/internals/construction.py", line 503, in dict_to_mgr
                  return arrays_to_mgr(arrays, columns, index, dtype=dtype, typ=typ, consolidate=copy)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/pandas/core/internals/construction.py", line 114, in arrays_to_mgr
                  index = _extract_index(arrays)
                          ^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/pandas/core/internals/construction.py", line 677, in _extract_index
                  raise ValueError("All arrays must be of the same length")
              ValueError: All arrays must be of the same length
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2251, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 279, in _generate_tables
                  raise e
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 242, in _generate_tables
                  pa_table = paj.read_json(
                             ^^^^^^^^^^^^^^
                File "pyarrow/_json.pyx", line 342, in pyarrow._json.read_json
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: JSON parse error: Invalid value. in row 0

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Android in the Wild (AITW)

This is a mirror of Google's Android in the Wild (AITW) dataset, re-hosted on Hugging Face for easier community access.

Original Source

Paper: Android in the Wild: A Large-Scale Dataset for Android Device Control
Original Repository: google-research/google-research/tree/master/android_in_the_wild

Dataset Description

Android in the Wild (AITW) is a large-scale dataset for Android device control. It contains human demonstrations of natural language instructions being carried out on Android devices. Each demonstration consists of a sequence of screenshots paired with corresponding actions (taps, swipes, types, etc.) and UI annotations.

Dataset Structure

The dataset is organized into 5 subsets, stored as gzip-compressed TFRecord files:

Subset	Shards	Description
`general`	321	General Android tasks
`google_apps`	8,688	Tasks on Google applications
`install`	1,052	App installation tasks
`single`	252	Single-step tasks
`web_shopping`	1,025	Web shopping tasks

Additionally, the splits/ directory contains JSON files defining train/test splits:

standard.json
unseen_android_version.json
unseen_domain.json
unseen_subject.json
unseen_verb.json

Data Format

Each TFRecord contains examples with the following fields:

image/encoded — screenshot image (encoded)
image/ui_annotations_ui_types — UI element type annotations (e.g., ICON_STOP, ICON_V_BACKWARD)
Additional action and metadata fields

Usage

import tensorflow as tf
import gzip

def read_tfrecord(file_path):
    with gzip.open(file_path, 'rb') as f:
        raw = f.read()
    dataset = tf.data.TFRecordDataset([file_path], compression_type='GZIP')
    return dataset

For detailed usage instructions, refer to the original repository.

License

This dataset is licensed under CC BY 4.0, following the original release.

Citation

@article{rawles2023android,
  title={Android in the Wild: A Large-Scale Dataset for Android Device Control},
  author={Rawles, Christopher and Li, Alice and Rodriguez, Daniel and Ber, Oriana and Zitkovich, Brianna},
  journal={arXiv preprint arXiv:2307.10088},
  year={2023}
}

Disclaimer

This is an unofficial mirror. All credit goes to the original authors at Google Research. This copy is provided solely to facilitate easier access for the research community.

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Paper for leosltl/Android-in-the-Wild

Android in the Wild: A Large-Scale Dataset for Android Device Control

Paper • 2307.10088 • Published Jul 19, 2023 • 12