Datasets:

datajuicer
/

redpajama-book-refined-by-data-juicer

Error code:   DatasetGenerationError
Exception:    ArrowNotImplementedError
Message:      Cannot write struct type 'meta' with no child field to Parquet. Consider adding a dummy child field.
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2011, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 583, in write_table
                  self._build_writer(inferred_schema=pa_table.schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 404, in _build_writer
                  self.pa_writer = self._WRITER_CLASS(self.stream, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/parquet/core.py", line 1010, in __init__
                  self.writer = _parquet.ParquetWriter(
                File "pyarrow/_parquet.pyx", line 2157, in pyarrow._parquet.ParquetWriter.__cinit__
                File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
              pyarrow.lib.ArrowNotImplementedError: Cannot write struct type 'meta' with no child field to Parquet. Consider adding a dummy child field.
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2027, in _prepare_split_single
                  num_examples, num_bytes = writer.finalize()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 602, in finalize
                  self._build_writer(self.schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 404, in _build_writer
                  self.pa_writer = self._WRITER_CLASS(self.stream, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/parquet/core.py", line 1010, in __init__
                  self.writer = _parquet.ParquetWriter(
                File "pyarrow/_parquet.pyx", line 2157, in pyarrow._parquet.ParquetWriter.__cinit__
                File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
              pyarrow.lib.ArrowNotImplementedError: Cannot write struct type 'meta' with no child field to Parquet. Consider adding a dummy child field.
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1529, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1154, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1027, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1122, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2038, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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meta dict	text string	stats dict	simhash float64
{}	"Produced by Suzanne Shell, Sjaani and PG Distributed Proofreaders\n\n\n\n\nTHE HOUSE ON THE BORDERL(...TRUNCATED)	{"alnum_ratio":0.7730060037,"avg_line_length":53.1530147895,"char_rep_ratio":0.0357377283,"flagged_w(...TRUNCATED)	16,879,431,143,088,323,000
{}	"This file was produced from images generously made available by the Bibliotheque nationale de Franc(...TRUNCATED)	{"alnum_ratio":0.7912897832,"avg_line_length":59.7947893757,"char_rep_ratio":0.0363826187,"flagged_w(...TRUNCATED)	3,644,732,410,104,741,400
{}	"Produced by Charles Aldarondo, Charlie Kirschner\nand the Online Distributed Proofreading Team.\n\n(...TRUNCATED)	{"alnum_ratio":0.7837232004,"avg_line_length":55.9332659252,"char_rep_ratio":0.0307179452,"flagged_w(...TRUNCATED)	11,089,683,488,658,866,000
{}	"Produced by Christine De Ryck, Stig M. Valstad, Suzanne L. Shell\nand PG Distributed Proofreaders\n(...TRUNCATED)	{"alnum_ratio":0.7885623647,"avg_line_length":58.9589676671,"char_rep_ratio":0.032107734,"flagged_wo(...TRUNCATED)	6,291,782,967,138,216,000
{}	"Produced by Ted Garvin, Dave Morgan and PG Distributed Proofreaders\n\n\n\n\nLA FIAMMETTA\n\nBY\n\n(...TRUNCATED)	{"alnum_ratio":0.7910396996,"avg_line_length":62.9837189374,"char_rep_ratio":0.0197705904,"flagged_w(...TRUNCATED)	6,492,970,723,819,643,000
{}	"Produced by Suzanne Shell, Sjaani and PG Distributed Proofreaders\n\n\n\n\nCARMILLA\n\nJ. Sheridan (...TRUNCATED)	{"alnum_ratio":0.7786372339,"avg_line_length":47.4699603538,"char_rep_ratio":0.0261364001,"flagged_w(...TRUNCATED)	7,426,944,496,859,436,000
{}	"Produced by Suzanne Shell, Danny Wool, Luiz Antonio de Souza,\nElisa Williams, Tonya Allen and PG D(...TRUNCATED)	{"alnum_ratio":0.7687656563,"avg_line_length":43.1778642555,"char_rep_ratio":0.0327301742,"flagged_w(...TRUNCATED)	14,936,655,909,178,157,000
{}	"Produced by Dennis McCarthy\n\n\n\n\n\n\n\n\n\nTHE DIVINE COMEDY\n\nOF DANTE ALIGHIERI\n(1265-1321)(...TRUNCATED)	{"alnum_ratio":0.7484757649,"avg_line_length":32.4585585586,"char_rep_ratio":0.0269562603,"flagged_w(...TRUNCATED)	16,855,008,483,676,178,000
{}	"Produced by Jonathan Ingram and PG Distributed Proofreaders\n\n\n\n\nTHE EULOGIES OF HOWARD.\n\nA V(...TRUNCATED)	{"alnum_ratio":0.8076707115,"avg_line_length":61.8861003861,"char_rep_ratio":0.0252866391,"flagged_w(...TRUNCATED)	15,541,307,282,155,172,000
{}	"Produced by Andrew Heath, Joshua Hutchinson, Audrey Longhurst\nand PG Distributed Proofreaders\n\n\(...TRUNCATED)	{"alnum_ratio":0.7702943221,"avg_line_length":37.780952381,"char_rep_ratio":0.1020916411,"flagged_wo(...TRUNCATED)	8,229,905,533,405,478,000

End of preview.

RedPajama -- Book (refined by Data-Juicer)

A refined version of Book dataset in RedPajama by Data-Juicer. Removing some "bad" samples from the original dataset to make it higher-quality.

This dataset is usually used to pretrain a Large Language Model.

Notice: Here is a small subset for previewing. The whole dataset is available here (About 91GB).

Dataset Information

Number of samples: 195,983 (Keep ~95.51% from the original dataset)

Refining Recipe

# global parameters
project_name: 'Data-Juicer-recipes-book'
dataset_path: '/path/to/your/dataset'  # path to your dataset directory or file
export_path: '/path/to/your/dataset.jsonl'

np: 50  # number of subprocess to process your dataset
open_tracer: true

# process schedule
# a list of several process operators with their arguments
process:
  - clean_email_mapper:
  - clean_links_mapper:
  - fix_unicode_mapper:
  - punctuation_normalization_mapper:
  - whitespace_normalization_mapper:

  - alphanumeric_filter:
      tokenization: false
      min_ratio: 0.55  # <3sigma (0.697)
      max_ratio: 0.854  # 3sigma
  - average_line_length_filter:  # for code
      max_len: 500  # >3sigma (364)
  - character_repetition_filter:
      rep_len: 10
      max_ratio: 0.2  # >3sigma (0.12)
  - flagged_words_filter:
      lang: en
      tokenization: true
      max_ratio: 0.00047  # 3sigma
  - language_id_score_filter:  # remove language filter
      min_score: 0.2
  - maximum_line_length_filter:  # for code
      max_len: 13381  # 3sigma
  - perplexity_filter:
      lang: en
      max_ppl: 6000  # <3sigma (16516)
  - special_characters_filter:
      max_ratio: 0.5  # >3sigma (0.32)
  - words_num_filter:
      lang: en
      tokenization: true
      min_num: 1000
      max_num: 539754  # 3sigma
  - word_repetition_filter:
      lang: en
      tokenization: true
      rep_len: 10
      max_ratio: 0.194  # 3sigma

  - document_simhash_deduplicator:
      tokenization: space
      window_size: 6
      lowercase: true
      ignore_pattern: '\p{P}'
      num_blocks: 6
      hamming_distance: 4

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