Datasets:

dinhanhx
/

google-wit-vi

Tasks:

Image-to-Text

Sub-tasks:

image-captioning

Languages:

Vietnamese

Size:

100M<n<1B

License:

Dataset card Files Files and versions Community

Dataset Viewer

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Split (3)

train

The dataset viewer is not available for this split.

Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.

Error code:   FeaturesError
Exception:    ParserError
Message:      Error tokenizing data. C error: Expected 17 fields in line 7, saw 25

Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 322, in compute
                  compute_first_rows_from_parquet_response(
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 88, in compute_first_rows_from_parquet_response
                  rows_index = indexer.get_rows_index(
                File "/src/libs/libcommon/src/libcommon/parquet_utils.py", line 640, in get_rows_index
                  return RowsIndex(
                File "/src/libs/libcommon/src/libcommon/parquet_utils.py", line 521, in __init__
                  self.parquet_index = self._init_parquet_index(
                File "/src/libs/libcommon/src/libcommon/parquet_utils.py", line 538, in _init_parquet_index
                  response = get_previous_step_or_raise(
                File "/src/libs/libcommon/src/libcommon/simple_cache.py", line 591, in get_previous_step_or_raise
                  raise CachedArtifactError(
              libcommon.simple_cache.CachedArtifactError: The previous step failed.
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 240, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2216, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1239, in _head
                  return _examples_to_batch(list(self.take(n)))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1389, in __iter__
                  for key, example in ex_iterable:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1044, in __iter__
                  yield from islice(self.ex_iterable, self.n)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 282, in __iter__
                  for key, pa_table in self.generate_tables_fn(**self.kwargs):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/csv/csv.py", line 195, in _generate_tables
                  for batch_idx, df in enumerate(csv_file_reader):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 1843, in __next__
                  return self.get_chunk()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 1985, in get_chunk
                  return self.read(nrows=size)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 1923, in read
                  ) = self._engine.read(  # type: ignore[attr-defined]
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/c_parser_wrapper.py", line 234, in read
                  chunks = self._reader.read_low_memory(nrows)
                File "parsers.pyx", line 850, in pandas._libs.parsers.TextReader.read_low_memory
                File "parsers.pyx", line 905, in pandas._libs.parsers.TextReader._read_rows
                File "parsers.pyx", line 874, in pandas._libs.parsers.TextReader._tokenize_rows
                File "parsers.pyx", line 891, in pandas._libs.parsers.TextReader._check_tokenize_status
                File "parsers.pyx", line 2061, in pandas._libs.parsers.raise_parser_error
              pandas.errors.ParserError: Error tokenizing data. C error: Expected 17 fields in line 7, saw 25

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Google WIT Vietnamese

This data repos contain extracted data from Google WIT. The extracted data is all for Vietnamese language.

Given x is a data point in the OG dataset which has keys following OG field_name, the criteria to filter is

criteria = lambda x: x.get("language", "") == "vi" and x.get("caption_reference_description", "")

Text-related details

All .tsv.gz files follow OG data files in terms of file names and file structures.

Train split

wit_v1.train.*.tsv.gz

Train data length of each file (not including the header),

Total 176752

Validation split

wit_v1.val.*.tsv.gz

Val data length of each file (not including the header),

Total 1466

Test split

wit_v1.test.*.tsv.gz

Test data length of each file (not including the header),

Total 1048

Image-related details

Image URL only

*.image_url_list.txt are simply lists of image urls from *.tsv.gz files

Image url length of each file (train, val, test, all)

Google Research has made sure that all sets don't share same exact images.

Downloaded Images

⚠ Please for the love of the gods, read this section carefully.

For all.index.fmt_id.image_url_list.tsv, from left to right, without headers, the columns are index, fmt_id, image_url. It is to map image_url (in all.image_url_list.txt) to fmt_id. It's for downloading images.

fmt_id is:

used to name images (with proper image extensions) in images/.
index but filled with 6 zeros

Downloading time was less than 36 hours with:

90 Mbps
Processor Intel(R) Core(TM) i7-8550U CPU @ 1.80GHz 1.99 GHz
No asynchronous

For fail.index.fmt_id.status.image_url_list.tsv, from left to right, without headers, the columns are index, fmt_id, status, image_url. It is to track image urls (during downloading) that are inaccessible.

3367 image urls returned 404 (status values). In other words, we were able to download 97.88839275% of images.

images/ folder takes disk space of:

215 GBs (uncompressed)
209 GBs (compressed)

We use Pillow to open each image to make sure that downloaded images are usable. We also log all faulty files in corrupted_image_list.json. There are less than 70 image files.

For corrupted_image_list.json, for each item in this list, the keys are file_name, error. file_name is fmt_id with extension but without images/. Some errors are either:

files exceed Pillow default limit
files are truncated

To actually load those files, the following code can be used to change Pillow behavior

from PIL import Image, ImageFile

# For very big image files
Image.MAX_IMAGE_PIXELS = None

# For truncated image files
ImageFile.LOAD_TRUNCATED_IMAGES = True

Zip images/ folder,

zip -r images.zip images/
zip images.zip --out spanned_images.zip -s 40g

https://superuser.com/questions/336219/how-do-i-split-a-zip-file-into-multiple-segments

Unzip spanned_images.* files,

zip -s 0 spanned_images.zip --out images.zip
unzip images.zip

https://unix.stackexchange.com/questions/40480/how-to-unzip-a-multipart-spanned-zip-on-linux

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