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Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code: FeaturesError
Exception: ValueError
Message: Failed to convert pandas DataFrame to Arrow Table from file gzip://16h-docstring-docstring_metric-False-0.json::hf://datasets/agaralon/ACDC-Runs@04c3e913c2f5cbb365f07cba39682feaa80b5289/16h-docstring-docstring_metric-False-0.json.gz.
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 233, 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 2998, 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 1918, 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 2093, in __iter__
for key, example in ex_iterable:
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1576, in __iter__
for key_example in islice(self.ex_iterable, self.n - ex_iterable_num_taken):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 279, in __iter__
for key, pa_table in self.generate_tables_fn(**gen_kwags):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 172, in _generate_tables
raise ValueError(
ValueError: Failed to convert pandas DataFrame to Arrow Table from file gzip://16h-docstring-docstring_metric-False-0.json::hf://datasets/agaralon/ACDC-Runs@04c3e913c2f5cbb365f07cba39682feaa80b5289/16h-docstring-docstring_metric-False-0.json.gz.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.
These are the runs from the paper Towards Automated Circuit Discovery for Mechanistic Interpretability (Arthur Conmy et al., 2023).
This repository contains the actual hypotheses that the various algorithms tested found. It's intended to help with reproducibility.
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