Error code:   JWTInvalidSignature
Exception:    InvalidSignatureError
Message:      Signature verification failed
Traceback:    Traceback (most recent call last):
                File "/src/libs/libapi/src/libapi/jwt_token.py", line 286, in validate_jwt
                  decoded = jwt.decode(
                      jwt=token,
                  ...<2 lines>...
                      options=options,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 368, in decode
                  decoded = self.decode_complete(
                      jwt,
                  ...<8 lines>...
                      leeway=leeway,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 265, in decode_complete
                  decoded = self._jws.decode_complete(
                      jwt,
                  ...<3 lines>...
                      detached_payload=detached_payload,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 270, in decode_complete
                  self._verify_signature(
                  ~~~~~~~~~~~~~~~~~~~~~~^
                      signing_input,
                      ^^^^^^^^^^^^^^
                  ...<4 lines>...
                      options=merged_options,
                      ^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 417, in _verify_signature
                  raise InvalidSignatureError("Signature verification failed")
              jwt.exceptions.InvalidSignatureError: Signature verification failed

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Vision-FLAN (191-task) — Arrow (images embedded)

The Vision-FLAN vision-flan_191-task_1k visual instruction-tuning set converted to a 🤗 datasets Arrow dataset with image bytes embedded. 186,103 examples spanning ~191 human-labeled vision tasks.

⚠️ This repo is a raw save_to_disk Arrow snapshot. The dataset viewer and load_dataset() do not work here — load it with load_from_disk as shown below.

Loading

from huggingface_hub import snapshot_download
from datasets import load_from_disk

path = snapshot_download("Ethlake/vision-flan", repo_type="dataset")
ds = load_from_disk(path)
print(ds)        # 186103 rows
print(ds[0])

Schema

column	type	notes
`images`	list[Image]	embedded bytes, decode to `PIL.Image` (typically 1 per row)
`texts`	list[{user, assistant}]	paired conversation turns (mostly single-turn)
`source_subset`	string	source task, e.g. `VQA_counting`, `HICO+human_activity_detection`

Example row:

{
 'images': [<PIL.Image RGB 427x640>],
 'texts': [
   {'user': 'This task tests your ability to count number of objects. '
            'Here is the question "How many chairs are in the photo?".',
    'assistant': 'two'},
 ],
 'source_subset': 'VQA_counting',
}

Notes

~191 distinct source_subset task types; filter on this column to slice by task.
Image bytes are embedded directly in the Arrow shards — self-contained, no separate image folders.
images is a list (multi-image capable) even though most examples contain a single image.

Source

Derived from Vision-Flan/vision-flan_191-task_1k ("Vision-Flan: Scaling Human-Labeled Tasks in Visual Instruction Tuning"). Please respect the original dataset's license and the licenses of its upstream image sources.

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