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lilac-wikitext-2-raw-v1

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train

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.

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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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