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The dataset viewer is not available for this dataset.
Cannot get the config names for the dataset.
Error code: ConfigNamesError
Exception: ReadTimeout
Message: (ReadTimeoutError("HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)"), '(Request ID: 79b0648c-f42a-4454-aa06-0997ed5edbc2)')
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 66, in compute_config_names_response
config_names = get_dataset_config_names(
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 161, in get_dataset_config_names
dataset_module = dataset_module_factory(
^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 1207, in dataset_module_factory
raise e1 from None
File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 1182, in dataset_module_factory
).get_module()
^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 598, in get_module
standalone_yaml_path = cached_path(
^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/utils/file_utils.py", line 180, in cached_path
).resolve_path(url_or_filename)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/huggingface_hub/hf_file_system.py", line 198, in resolve_path
repo_and_revision_exist, err = self._repo_and_revision_exist(repo_type, repo_id, revision)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/huggingface_hub/hf_file_system.py", line 125, in _repo_and_revision_exist
self._api.repo_info(
File "/usr/local/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
return fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/huggingface_hub/hf_api.py", line 2816, in repo_info
return method(
^^^^^^^
File "/usr/local/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
return fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/huggingface_hub/hf_api.py", line 2673, in dataset_info
r = get_session().get(path, headers=headers, timeout=timeout, params=params)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/requests/sessions.py", line 602, in get
return self.request("GET", url, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/requests/sessions.py", line 589, in request
resp = self.send(prep, **send_kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/requests/sessions.py", line 703, in send
r = adapter.send(request, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/huggingface_hub/utils/_http.py", line 96, in send
return super().send(request, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/requests/adapters.py", line 690, in send
raise ReadTimeout(e, request=request)
requests.exceptions.ReadTimeout: (ReadTimeoutError("HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)"), '(Request ID: 79b0648c-f42a-4454-aa06-0997ed5edbc2)')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.
100K dataset schema Articles
Sample raw:
{"id": 13575, "title": "The Complete Guide to kubernetes", "content": "Asynchronous processing enables systems to remain responsive under heavy load. Message queues, event-driven architectures, and background workers are common patterns for achieving this.\n\nScalability is not just about handling more traffic \u2014 it's about maintaining consistent performance as complexity grows. Horizontal scaling introduces challenges around data consistency and coordination between nodes.\n\nThis article provides a comprehensive overview of the topic at hand. We will explore the key concepts, practical applications, and common pitfalls that developers and practitioners encounter when working with this technology.\n\nThe design patterns we choose today become the constraints we live with tomorrow. Choosing the right abstraction layer early saves tremendous pain later.", "tags": ["java", "frontend"], "views": 48404, "published_at": "2022-10-18", "author": "zRGRWATSVjABdmmSXvZG"}
{"id": 615621, "title": "data pipelines: Best Practices and Common Pitfalls", "content": "In practice, there are several approaches one can take when implementing this solution. The first approach involves a direct method that is straightforward but may not scale well under heavy load. The second uses a more sophisticated technique that requires additional infrastructure but provides better performance guarantees.\n\nScalability is not just about handling more traffic \u2014 it's about maintaining consistent performance as complexity grows. Horizontal scaling introduces challenges around data consistency and coordination between nodes.\n\nThe design patterns we choose today become the constraints we live with tomorrow. Choosing the right abstraction layer early saves tremendous pain later.\n\nAsynchronous processing enables systems to remain responsive under heavy load. Message queues, event-driven architectures, and background workers are common patterns for achieving this.", "tags": ["python", "postgres", "grpc"], "views": 68564, "published_at": "2021-11-12", "author": "bUADJclhdIbRNLGaJSCD"}
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