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The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code: FeaturesError
Exception: ValueError
Message: Not able to read records in the JSON file at hf://datasets/lilacai/lilac-wikitext-2-raw-v1@85e5f78663be0e0deea80d01f8d241f8f228d06e/lilac/wikitext-2-raw-v1/text/lilac/legal-termination/gte-small/signal_manifest.json. You should probably indicate the field of the JSON file containing your records. This JSON file contain the following fields: ['files', 'parquet_id', 'data_schema', 'signal', 'enriched_path', 'py_version']. Select the correct one and provide it as `field='XXX'` to the dataset loading method.
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 241, 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 2216, 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 1239, 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 1389, in __iter__
for key, example in ex_iterable:
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1044, in __iter__
yield from islice(self.ex_iterable, self.n)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 282, in __iter__
for key, pa_table in self.generate_tables_fn(**self.kwargs):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 170, in _generate_tables
raise ValueError(
ValueError: Not able to read records in the JSON file at hf://datasets/lilacai/lilac-wikitext-2-raw-v1@85e5f78663be0e0deea80d01f8d241f8f228d06e/lilac/wikitext-2-raw-v1/text/lilac/legal-termination/gte-small/signal_manifest.json. You should probably indicate the field of the JSON file containing your records. This JSON file contain the following fields: ['files', 'parquet_id', 'data_schema', 'signal', 'enriched_path', 'py_version']. Select the correct one and provide it as `field='XXX'` to the dataset loading method.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)
This dataset is generated by Lilac for a HuggingFace Space: huggingface.co/spaces/lilacai/lilac.
Original dataset: https://huggingface.co/datasets/wikitext
Lilac dataset config:
name: wikitext-2-raw-v1
source:
dataset_name: wikitext
config_name: wikitext-2-raw-v1
source_name: huggingface
embeddings:
- path: text
embedding: gte-small
signals:
- path: text
signal:
signal_name: near_dup
- path: text
signal:
signal_name: pii
- path: text
signal:
signal_name: lang_detection
- path: text
signal:
signal_name: text_statistics
- path: text
signal:
embedding: gte-small
namespace: lilac
concept_name: legal-termination
signal_name: concept_score
- path: text
signal:
embedding: gte-small
namespace: lilac
concept_name: negative-sentiment
signal_name: concept_score
- path: text
signal:
embedding: gte-small
namespace: lilac
concept_name: non-english
signal_name: concept_score
- path: text
signal:
embedding: gte-small
namespace: lilac
concept_name: positive-sentiment
signal_name: concept_score
- path: text
signal:
embedding: gte-small
namespace: lilac
concept_name: profanity
signal_name: concept_score
- path: text
signal:
embedding: gte-small
namespace: lilac
concept_name: question
signal_name: concept_score
- path: text
signal:
embedding: gte-small
namespace: lilac
concept_name: source-code
signal_name: concept_score
- path: text
signal:
embedding: gte-small
namespace: lilac
concept_name: toxicity
signal_name: concept_score
- path: text
signal:
embedding: gte-small
namespace: lilac
concept_name: legal-termination
signal_name: concept_score
- path: text
signal:
embedding: gte-small
namespace: lilac
concept_name: negative-sentiment
signal_name: concept_score
- path: text
signal:
embedding: gte-small
namespace: lilac
concept_name: non-english
signal_name: concept_score
- path: text
signal:
embedding: gte-small
namespace: lilac
concept_name: positive-sentiment
signal_name: concept_score
- path: text
signal:
embedding: gte-small
namespace: lilac
concept_name: profanity
signal_name: concept_score
- path: text
signal:
embedding: gte-small
namespace: lilac
concept_name: question
signal_name: concept_score
- path: text
signal:
embedding: gte-small
namespace: lilac
concept_name: source-code
signal_name: concept_score
- path: text
signal:
embedding: gte-small
namespace: lilac
concept_name: toxicity
signal_name: concept_score
- path: text
signal:
signal_name: cluster_dbscan
- path: text
signal:
embedding: gte-small
signal_name: cluster_hdbscan
settings:
ui:
media_paths:
- text
markdown_paths: []
tags:
- machine-learning
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