davanstrien/autotrain-map_no_map_twitter_demo-39701103400
Image Classification • Updated • 2 • 1
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 failedNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
This dataset has been automatically processed by AutoTrain for project map_no_map_twitter_demo.
The BCP-47 code for the dataset's language is unk.
A sample from this dataset looks as follows:
[
{
"image": "<260x365 RGB PIL image>",
"target": 1,
"feat_created_at": "2023-03-08T13:56:12.283125Z",
"feat_lead_time": 0.768,
"feat_annotation_id": 23,
"feat_id": 24,
"feat_annotator": 1,
"feat_updated_at": "2023-03-08T13:56:12.283151Z"
},
{
"image": "<639x1298 RGB PIL image>",
"target": 0,
"feat_created_at": "2023-03-08T13:59:10.554628Z",
"feat_lead_time": 0.533,
"feat_annotation_id": 87,
"feat_id": 88,
"feat_annotator": 1,
"feat_updated_at": "2023-03-08T13:59:10.554650Z"
}
]
The dataset has the following fields (also called "features"):
{
"image": "Image(decode=True, id=None)",
"target": "ClassLabel(names=['map', 'no_map'], id=None)",
"feat_created_at": "Value(dtype='string', id=None)",
"feat_lead_time": "Value(dtype='float64', id=None)",
"feat_annotation_id": "Value(dtype='int64', id=None)",
"feat_id": "Value(dtype='int64', id=None)",
"feat_annotator": "Value(dtype='int64', id=None)",
"feat_updated_at": "Value(dtype='string', id=None)"
}
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
|---|---|
| train | 72 |
| valid | 19 |