The dataset viewer is not available for this subset.
Cannot get the split names for the config 'default' of the dataset.
Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/mongoengine/queryset/base.py", line 269, in get
                  result = next(queryset)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/mongoengine/queryset/base.py", line 1608, in __next__
                  raw_doc = next(self._cursor)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pymongo/cursor.py", line 1267, in next
                  raise StopIteration
              StopIteration
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/libs/libcommon/src/libcommon/simple_cache.py", line 516, in get_response_with_details
                  CachedResponseDocument.objects(kind=kind, dataset=dataset, config=config, split=split)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/mongoengine/queryset/base.py", line 272, in get
                  raise queryset._document.DoesNotExist(msg)
              libcommon.simple_cache.DoesNotExist: CachedResponseDocument matching query does not exist.
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 159, in compute
                  compute_split_names_from_info_response(
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 131, in compute_split_names_from_info_response
                  config_info_response = get_previous_step_or_raise(kind="config-info", dataset=dataset, config=config)
                File "/src/libs/libcommon/src/libcommon/simple_cache.py", line 565, in get_previous_step_or_raise
                  response = get_response_with_details(kind=kind, dataset=dataset, config=config, split=split)
                File "/src/libs/libcommon/src/libcommon/simple_cache.py", line 529, in get_response_with_details
                  raise CachedArtifactNotFoundError(kind=kind, dataset=dataset, config=config, split=split) from e
              libcommon.simple_cache.CachedArtifactNotFoundError: Cache entry does not exist: kind='config-info' dataset='csarron/4m-img-caps' config='default' split=None
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 499, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/arrow/arrow.py", line 50, in _split_generators
                  self.info.features = datasets.Features.from_arrow_schema(pa.ipc.open_stream(f).schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/ipc.py", line 190, in open_stream
                  return RecordBatchStreamReader(source, options=options,
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/ipc.py", line 52, in __init__
                  self._open(source, options=options, memory_pool=memory_pool)
                File "pyarrow/ipc.pxi", line 974, in pyarrow.lib._RecordBatchStreamReader._open
                File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
              OSError: Invalid flatbuffers message.
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 75, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 572, in get_dataset_split_names
                  info = get_dataset_config_info(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 504, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

YAML Metadata Warning: empty or missing yaml metadata in repo card (https://huggingface.co/docs/hub/datasets-cards)

see read_pyarrow.py for how to read one pyarrow file.

example PyTorch dataset:

from torch.utils.data import Dataset

class ImageCaptionArrowDataset(Dataset):
    def __init__(
        self,
        dataset_file,
        tokenizer,
    ):

        import pyarrow as pa

        data = [pa.ipc.open_file(pa.memory_map(f, "rb")).read_all() for f in glob.glob(dataset_file)]
        self.data = pa.concat_tables(data)
        # do other initialization, like init image preprocessing fn, 

    def __getitem__(self, index):
        # item_id = self.data["id"][index].as_py()
        text = self.data["text"][index].as_py() # get text
        if isinstance(text, list):
            text = random.choice(text)

        img_bytes = self.data["image"][index].as_py() # get image bytes
        
        # do some processing with image and text, return the features
        
        
        # img_feat = self.image_bytes_to_tensor(img_bytes)
        # inputs = self.tokenizer(
        #     text,
        #     padding="max_length",
        #     max_length=self.max_text_len,
        #     truncation=True,
        #     return_token_type_ids=True,
        #     return_attention_mask=True,
        #     add_special_tokens=True,
        #     return_tensors="pt",
        # )
        # input_ids = inputs.input_ids.squeeze(0)
        # attention_mask = inputs.attention_mask.squeeze(0)
        # return {
        #     # "item_ids": item_id,
        #     "text_ids": input_ids,
        #     "input_ids": input_ids,
        #     "text_masks": attention_mask,
        #     "pixel_values": img_feat,
        # }
    def __len__(self):
        return len(self.data)

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