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Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      JSON parse error: Column(/ner/[]/[]) changed from number to string in row 0
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 183, in _generate_tables
                  df = pandas_read_json(f)
                       ^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 38, in pandas_read_json
                  return pd.read_json(path_or_buf, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/pandas/io/json/_json.py", line 815, in read_json
                  return json_reader.read()
                         ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/pandas/io/json/_json.py", line 1014, in read
                  obj = self._get_object_parser(self.data)
                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/pandas/io/json/_json.py", line 1040, in _get_object_parser
                  obj = FrameParser(json, **kwargs).parse()
                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/pandas/io/json/_json.py", line 1176, in parse
                  self._parse()
                File "/usr/local/lib/python3.12/site-packages/pandas/io/json/_json.py", line 1392, in _parse
                  ujson_loads(json, precise_float=self.precise_float), dtype=None
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
              ValueError: Trailing data
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 243, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 3608, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2368, in _head
                  return next(iter(self.iter(batch_size=n)))
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2573, in iter
                  for key, example in iterator:
                                      ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2060, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2082, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 544, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 383, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 186, in _generate_tables
                  raise e
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 160, in _generate_tables
                  pa_table = paj.read_json(
                             ^^^^^^^^^^^^^^
                File "pyarrow/_json.pyx", line 342, in pyarrow._json.read_json
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: JSON parse error: Column(/ner/[]/[]) changed from number to string in row 0

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Betterdata Annotated Multilingual NER/PII

Dataset Summary

This dataset contains multilingual, annotated NER/PII spans across 13 languages with 60+ label classes spanning PII, PHI, PCI, and general entity types. It is designed to train and evaluate privacy-preserving NER models.

Data Sources

  • bloomberg_financial_news_annotated (data/augmented/bloomberg_financial_news_annotated.jsonl)
  • c4_multilingual_annotated (data/augmented/c4_multilingual_annotated.jsonl)
  • finewiki_annotated (data/augmented/finewiki_annotated.jsonl)
  • pleias_sec_annotated (data/augmented/pleias_sec_annotated.jsonl)
  • pubmed_common_pile_annotated (data/augmented/pubmed_common_pile_annotated.jsonl)
  • pubmed_medrag_annotated (data/augmented/pubmed_medrag_annotated.jsonl)
  • wiki40b_annotated (data/augmented/wiki40b_annotated.jsonl)
  • wikiann_annotated (data/augmented/wikiann_annotated.jsonl)
  • wikipedia_annotated (data/augmented/wikipedia_annotated.jsonl)
  • wikisource_annotated (data/augmented/wikisource_annotated.jsonl)

Annotation Notes

  • LLMs are instructed for high recall, so some noise is expected.
  • Entities must appear verbatim in text; out-of-schema labels are discarded.
  • Outputs are normalized into GLiNER token-span format for training.

Label Schema

See label_schema.json for the full list of labels, categories, and descriptions.

Statistics

  • Total records: 387736
  • Total entities: 756419
  • Train/Validation/Test: 348958 / 19382 / 19396
  • Label coverage: 84 / 88
  • Languages: Chinese, Dutch, English, French, German, Indonesian, Italian, Japanese, Korean, Spanish, Swedish, Vietnamese

Intended Use

Training and evaluation for multilingual NER/PII detection and redaction.

Limitations

  • LLM-annotated data may contain noise.
  • Coverage varies by language and domain.
  • Some labels are sparse and may require additional sampling.

License

Apache-2.0. Verify that source corpus licenses are compatible before redistribution.

Citation

If you use this dataset, cite the accompanying dataset card and release date: 2026-02-12.

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