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Datasets:
samuki-hf
/
units-surprisal

Languages:
English
Tags:
eye-tracking
reading-times
psycholinguistics
surprisal
meco
License:
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The dataset generation failed because of a cast error
Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 15 new columns ({'trialid', 'refix', 'trial.nwords', 'nblink', 'nrun', 'uniform_id', 'rate', 'nfix', 'firstpass', 'trial', 'sac', 'lang', 'skip', 'total', 'reg'}) and 9 missing columns ({'x', 'letternum', 'right_bounded_time', 'fixid', 'y', 'stop', 'letter', 'dur', 'start'}).

This happened while the csv dataset builder was generating data using

hf://datasets/samuki-hf/units-surprisal/meco_source/passage_data_version1.3.csv (at revision 1230170c0205c6bc5ba145400ca657ce78edfac8), ['hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/meco_preproc/en/en_101_1.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/meco_preproc/en/en_101_10.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/meco_preproc/en/en_101_11.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/meco_preproc/en/en_101_12.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/meco_preproc/en/en_101_2.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/meco_preproc/en/en_101_3.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/meco_preproc/en/en_101_4.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/meco_preproc/en/en_101_5.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/meco_preproc/en/en_101_6.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/meco_preproc/en/en_101_7.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/meco_preproc/en/en_101_8.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/meco_preproc/en/en_101_9.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/meco_preproc/en/en_102_1.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/meco_preproc/en/en_102_10.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/meco_preproc/en/en_102_11.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/meco_preproc/en/en_102_12.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/meco_preproc/en/en_102_2.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/meco_preproc/en/en_102_3.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/meco_preproc/en/en_102_4.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/meco_preproc/en/en_102_5.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/meco_preproc/en/en_102_6.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/meco_preproc/en/en_102_7.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/meco_preproc/en/en_102_8.csv', 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'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_91_1_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_91_2_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_91_3_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_91_4_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_91_5_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_91_6_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_91_7_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_91_8_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_91_9_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_93_10_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_93_11_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_93_12_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_93_1_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_93_2_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_93_3_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_93_4_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_93_5_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_93_6_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_93_7_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_93_8_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_93_9_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_94_10_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_94_11_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_94_12_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_94_1_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_94_2_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_94_3_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_94_4_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_94_5_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_94_6_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_94_7_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_94_8_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_94_9_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_95_10_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_95_11_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_95_12_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_95_1_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_95_2_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_95_3_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_95_4_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_95_5_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_95_6_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_95_7_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_95_8_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_95_9_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_97_10_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_97_11_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_97_12_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_97_1_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_97_2_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_97_3_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_97_4_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_97_5_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_97_6_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_97_7_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_97_8_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_97_9_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_98_10_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_98_11_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_98_12_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_98_1_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_98_2_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_98_3_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_98_4_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_98_5_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_98_6_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_98_7_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_98_8_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_98_9_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_99_10_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_99_11_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_99_12_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_99_1_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_99_2_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_99_3_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_99_4_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_99_5_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_99_6_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_99_7_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_99_8_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/subject_units/whitespace_trailing_en/en/en_99_9_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/surprisal/character_leading_en_gpt2_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/surprisal/model_tokens_leading_en_gpt2_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/surprisal/ptb_leading_en_gpt2_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/surprisal/whitespace_leading_en_gpt2_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/surprisal/whitespace_trailing_en_gpt2_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/text_units/character_leading_en_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/text_units/model_tokens_leading_en_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/text_units/ptb_leading_en_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/text_units/whitespace_leading_en_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/text_units/whitespace_trailing_en_units.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/unigram_probs/unigram_character_leading_en_gpt2.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/unigram_probs/unigram_model_tokens_leading_en_gpt2.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/unigram_probs/unigram_ptb_leading_en_gpt2.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/unigram_probs/unigram_whitespace_leading_en_gpt2.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/unigram_probs/unigram_whitespace_trailing_en_gpt2.csv']

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1837, in _prepare_split_single
                  writer.write_table(table)
                  ~~~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 765, in write_table
                  self._write_table(pa_table, writer_batch_size=writer_batch_size)
                  ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              uniform_id: string
              lang: string
              trialid: int64
              trial: string
              trial.nwords: int64
              rate: int64
              nfix: int64
              nrun: int64
              sac: double
              skip: double
              refix: double
              reg: double
              firstpass: int64
              total: int64
              nblink: int64
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1930
              to
              {'fixid': Value('int64'), 'x': Value('float64'), 'y': Value('float64'), 'letter': Value('string'), 'letternum': Value('int64'), 'start': Value('float64'), 'stop': Value('float64'), 'dur': Value('float64'), 'right_bounded_time': Value('float64')}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1839, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
                  ...<4 lines>...
                  )
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 15 new columns ({'trialid', 'refix', 'trial.nwords', 'nblink', 'nrun', 'uniform_id', 'rate', 'nfix', 'firstpass', 'trial', 'sac', 'lang', 'skip', 'total', 'reg'}) and 9 missing columns ({'x', 'letternum', 'right_bounded_time', 'fixid', 'y', 'stop', 'letter', 'dur', 'start'}).
              
              This happened while the csv dataset builder was generating data using
              
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'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/unigram_probs/unigram_character_leading_en_gpt2.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/unigram_probs/unigram_model_tokens_leading_en_gpt2.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/unigram_probs/unigram_ptb_leading_en_gpt2.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/unigram_probs/unigram_whitespace_leading_en_gpt2.csv', 'hf://datasets/samuki-hf/units-surprisal@1230170c0205c6bc5ba145400ca657ce78edfac8/unigram_probs/unigram_whitespace_trailing_en_gpt2.csv']
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

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232
12.311
null
212
null
54
4.711
1.841
c
240
12.548
null
318
null
56
4.361
1.851
f
238
13.079
null
187
null
59
7.951
1.861
r
262
13.749
null
273
null
64
10.721
1.961
o
280
14.994
null
370
null
65
10.981
1.921
r
282
15.372
null
396
null
66
11.721
1.891
287
15.787
null
170
null
67
12.201
1.941
s
290
15.972
null
162
null
68
13.231
1.981
e
297
16.155
null
182
null
69
13.711
2.071
e
300
16.352
null
168
null
71
15.601
2.151
i
313
16.762
null
329
null
73
17.331
2.091
324
17.233
null
97
null
76
3.161
2.341
p
343
17.81
null
154
null
77
2.701
2.411
n
340
18.081
null
214
null
85
9.371
2.461
s
384
19.803
null
122
null
86
10.341
2.511
c
391
19.947
null
209
null
87
11.681
2.521
t
400
20.186
null
188
null
88
12.451
2.611
t
405
20.395
null
253
null
89
14.311
2.581
x
417
20.685
null
292
null
90
14.811
2.541
n
421
20.995
null
92
null
91
15.311
2.501
t
424
21.105
null
131
null
99
6.431
2.831
b
464
23.076
null
200
null
100
6.631
2.921
o
465
23.417
null
104
null
101
7.821
2.971
h
473
23.544
null
197
null
102
8.321
3.041
g
476
23.758
null
152
null
103
10.081
3.111
i
488
23.943
null
371
null
104
9.371
3.101
t
483
24.337
null
139
null
105
8.531
3.041
478
24.5
null
330
null
106
8.231
3.041
g
476
24.841
null
121
null
107
10.051
3.041
i
488
24.992
null
257
null
111
14.941
3.131
521
26.17
null
286
null
112
16.061
3.011
n
528
26.486
null
95
null
113
16.771
3.001
o
533
26.602
null
139
null
114
17.191
3.051
535
26.755
null
110
null
119
3.201
3.371
o
553
27.88
null
260
null
121
5.421
3.321
n
568
28.36
null
309
null
122
7.331
3.411
h
581
28.704
null
166
null
123
8.011
3.481
r
585
28.893
null
206
null
124
8.721
3.541
590
29.121
null
364
null
125
9.521
3.521
o
595
29.509
null
208
null
126
9.781
3.571
n
597
29.727
null
270
null
127
10.801
3.571
l
604
30.029
null
148
null
128
11.611
3.631
t
609
30.198
null
410
null
131
14.271
3.861
739
31.442
null
307
null
136
2.741
3.871
n
662
33.012
null
130
null
139
4.631
3.881
h
675
33.606
null
111
null
142
2.541
3.861
i
661
34.219
null
189
null
143
6.401
3.991
686
34.456
null
145
null
144
7.421
3.951
n
693
34.627
null
282
null
145
8.291
3.981
e
699
34.933
null
146
null
146
9.671
3.981
l
708
35.108
null
194
null
147
10.911
4.011
m
717
35.332
null
167
null
148
10.011
4.021
710
35.527
null
139
null
149
12.901
4.091
a
730
35.713
null
230
null
150
15.101
4.111
745
35.981
null
181
null
152
15.741
4.091
i
749
36.4
null
141
null
153
3.041
4.291
s
771
36.639
null
152
null
156
3.611
4.461
i
775
37.427
null
206
null
157
5.661
4.531
789
37.666
null
267
null
158
5.421
4.491
s
787
37.944
null
225
null
159
6.511
4.501
g
794
38.195
null
223
null
160
7.601
4.441
801
38.443
null
101
null
161
7.211
4.491
t
799
38.689
null
152
null
162
2.661
4.361
o
769
38.892
null
266
null
163
2.231
4.511
d
766
39.181
null
158
null
164
5.171
4.391
i
785
39.379
null
213
null
165
6.911
4.421
d
797
39.626
null
266
null
166
9.311
4.461
e
813
39.935
null
194
null
167
9.731
4.451
i
816
40.144
null
185
null
168
8.921
4.431
810
40.356
null
206
null
169
9.651
4.441
h
815
40.583
null
207
null
170
10.091
4.421
h
818
40.807
null
206
null
End of preview.

units-surprisal: English data and pipeline intermediates

Everything needed to reproduce the English GAMM results of the units-surprisal paper (code). The full multilingual MECO data is in the companion dataset samuki-hf/meco-char.

Contents (place under src/units_surprisal/data/ in the code repo)

Folder Pipeline stage Description
meco_source/ stage 0 MECO source CSVs (passage_data_version1.3.csv, sentences.csv, supp_texts.csv), copied to the data root by download_data.py.
meco_preproc/en/ input Corrected per-(reader, text) English fixation CSVs.
meco_preproc_raw/en/ input Raw English fixation CSVs (before corrections).
meco_stimulus/ input Stimulus metadata (sentences_by_char.csv, text images).
raw_texts/raw_text_en*.csv stage 1 Extracted English stimulus text.
text_units/ stage 2 Transducer-defined unit boundaries per scheme.
subject_units/ stage 3 Per-subject fixation-to-unit aggregations.
subject_means/ stage 4 Cross-subject mean reading times per unit.
surprisal/ stage 5 Per-unit LM (GPT-2) marginal surprisal.
unigram_probs/ baseline Unigram surprisal baseline probabilities.

Unit inventories: whitespace (leading/trailing), PTB, GPT-2 model tokens, character. The GAMM analysis (src/units_surprisal/analysis/run_gamm.R) reads subject_units/, surprisal/, and unigram_probs/.

Source

Derived from MECO (Siegelman et al., 2022). Refer to the original MECO license and terms for the underlying eye-tracking data.
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