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Split (1)

train

The dataset viewer is not available for this split.

Cannot load the dataset split (in streaming mode) to extract the first rows.

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
_soc_127: struct<doc_id: string, doc_id_field: string, input_shard: string, phase: string, source_family: stri (... 26 chars omitted)
  child 0, doc_id: string
  child 1, doc_id_field: string
  child 2, input_shard: string
  child 3, phase: string
  child 4, source_family: string
  child 5, source_folder: string
id: string
metadata: struct<cc_dump: string, dolma2_qc: struct<0: double, 1: double>, exact_duplicates: int64, lang: stru (... 1196 chars omitted)
  child 0, cc_dump: string
  child 1, dolma2_qc: struct<0: double, 1: double>
      child 0, 0: double
      child 1, 1: double
  child 2, exact_duplicates: int64
  child 3, lang: struct<en: double>
      child 0, en: double
  child 4, madlad: struct<num_sentences: int64, rule.2: list<item: int64>, rule.5: list<item: int64>, status: string>
      child 0, num_sentences: int64
      child 1, rule.2: list<item: int64>
          child 0, item: int64
      child 2, rule.5: list<item: int64>
          child 0, item: int64
      child 3, status: string
  child 5, minhash: struct<cc_id: int64, cc_idx: int64, cc_size: int64>
      child 0, cc_id: int64
      child 1, cc_idx: int64
      child 2, cc_size: int64
  child 6, original_word_count: int64
  child 7, sa_remove_ranges: list<item: list<item: int64>>
      child 0, item: list<item: int64>
          child 0, item: int64
  child 8, text_hash: string
  child 9, warc_content_type: string
  child 10, warc_date: string
  child 11, warc_url: string
  child 12, weborganizer: struct<__
...
e
      child 3, __label__fashion_and_beauty: double
      child 4, __label__finance_and_business: double
      child 5, __label__games: double
      child 6, __label__health: double
      child 7, __label__social_life: double
      child 8, __label__software: double
      child 9, __label__travel_and_tourism: double
      child 10, __label__crime_and_law: double
      child 11, __label__literature: double
      child 12, __label__sports_and_fitness: double
      child 13, __label__politics: double
      child 14, __label__religion: double
      child 15, __label__history_and_geography: double
      child 16, __label__home_and_hobbies: double
      child 17, __label__industrial: double
      child 18, __label__science_math_and_technology: double
      child 19, __label__food_and_dining: double
      child 20, __label__education_and_jobs: double
      child 21, __label__software_development: double
      child 22, __label__electronics_and_hardare: double
      child 23, __label__transportation: double
  child 13, weborganizer_max: string
text: string
WORKING_SAMPLE_TOKEN_FLOOR_PER_BIN: int64
WORKING_SAMPLE_COVERED_BIN_COUNT: int64
WORKING_SAMPLE_MIN_TOKEN_COUNT: int64
WORKING_SAMPLE_UNDERFILLED_BIN_COUNT: int64
WORKING_SAMPLE_REALIZED_DOC_COUNT: int64
WORKING_SAMPLE_REALIZED_TOKEN_TOTAL: int64
WORKING_SAMPLE_GLOBAL_TOKEN_BUDGET: null
WORKING_SAMPLE_DOCS_PER_BIN: int64
WORKING_SAMPLE_SAMPLING_SEED: int64
WORKING_SAMPLE_MAX_TOKEN_COUNT: null
WORKING_SAMPLE_TOTAL_BIN_COUNT: int64
to
{'WORKING_SAMPLE_TOKEN_FLOOR_PER_BIN': Value('int64'), 'WORKING_SAMPLE_DOCS_PER_BIN': Value('int64'), 'WORKING_SAMPLE_GLOBAL_TOKEN_BUDGET': Value('null'), 'WORKING_SAMPLE_MIN_TOKEN_COUNT': Value('int64'), 'WORKING_SAMPLE_MAX_TOKEN_COUNT': Value('null'), 'WORKING_SAMPLE_REALIZED_TOKEN_TOTAL': Value('int64'), 'WORKING_SAMPLE_REALIZED_DOC_COUNT': Value('int64'), 'WORKING_SAMPLE_UNDERFILLED_BIN_COUNT': Value('int64'), 'WORKING_SAMPLE_COVERED_BIN_COUNT': Value('int64'), 'WORKING_SAMPLE_TOTAL_BIN_COUNT': Value('int64'), 'WORKING_SAMPLE_SAMPLING_SEED': Value('int64')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/utils.py", line 478, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2815, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2352, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2377, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 419, 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 310, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 130, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              _soc_127: struct<doc_id: string, doc_id_field: string, input_shard: string, phase: string, source_family: stri (... 26 chars omitted)
                child 0, doc_id: string
                child 1, doc_id_field: string
                child 2, input_shard: string
                child 3, phase: string
                child 4, source_family: string
                child 5, source_folder: string
              id: string
              metadata: struct<cc_dump: string, dolma2_qc: struct<0: double, 1: double>, exact_duplicates: int64, lang: stru (... 1196 chars omitted)
                child 0, cc_dump: string
                child 1, dolma2_qc: struct<0: double, 1: double>
                    child 0, 0: double
                    child 1, 1: double
                child 2, exact_duplicates: int64
                child 3, lang: struct<en: double>
                    child 0, en: double
                child 4, madlad: struct<num_sentences: int64, rule.2: list<item: int64>, rule.5: list<item: int64>, status: string>
                    child 0, num_sentences: int64
                    child 1, rule.2: list<item: int64>
                        child 0, item: int64
                    child 2, rule.5: list<item: int64>
                        child 0, item: int64
                    child 3, status: string
                child 5, minhash: struct<cc_id: int64, cc_idx: int64, cc_size: int64>
                    child 0, cc_id: int64
                    child 1, cc_idx: int64
                    child 2, cc_size: int64
                child 6, original_word_count: int64
                child 7, sa_remove_ranges: list<item: list<item: int64>>
                    child 0, item: list<item: int64>
                        child 0, item: int64
                child 8, text_hash: string
                child 9, warc_content_type: string
                child 10, warc_date: string
                child 11, warc_url: string
                child 12, weborganizer: struct<__
              ...
              e
                    child 3, __label__fashion_and_beauty: double
                    child 4, __label__finance_and_business: double
                    child 5, __label__games: double
                    child 6, __label__health: double
                    child 7, __label__social_life: double
                    child 8, __label__software: double
                    child 9, __label__travel_and_tourism: double
                    child 10, __label__crime_and_law: double
                    child 11, __label__literature: double
                    child 12, __label__sports_and_fitness: double
                    child 13, __label__politics: double
                    child 14, __label__religion: double
                    child 15, __label__history_and_geography: double
                    child 16, __label__home_and_hobbies: double
                    child 17, __label__industrial: double
                    child 18, __label__science_math_and_technology: double
                    child 19, __label__food_and_dining: double
                    child 20, __label__education_and_jobs: double
                    child 21, __label__software_development: double
                    child 22, __label__electronics_and_hardare: double
                    child 23, __label__transportation: double
                child 13, weborganizer_max: string
              text: string
              WORKING_SAMPLE_TOKEN_FLOOR_PER_BIN: int64
              WORKING_SAMPLE_COVERED_BIN_COUNT: int64
              WORKING_SAMPLE_MIN_TOKEN_COUNT: int64
              WORKING_SAMPLE_UNDERFILLED_BIN_COUNT: int64
              WORKING_SAMPLE_REALIZED_DOC_COUNT: int64
              WORKING_SAMPLE_REALIZED_TOKEN_TOTAL: int64
              WORKING_SAMPLE_GLOBAL_TOKEN_BUDGET: null
              WORKING_SAMPLE_DOCS_PER_BIN: int64
              WORKING_SAMPLE_SAMPLING_SEED: int64
              WORKING_SAMPLE_MAX_TOKEN_COUNT: null
              WORKING_SAMPLE_TOTAL_BIN_COUNT: int64
              to
              {'WORKING_SAMPLE_TOKEN_FLOOR_PER_BIN': Value('int64'), 'WORKING_SAMPLE_DOCS_PER_BIN': Value('int64'), 'WORKING_SAMPLE_GLOBAL_TOKEN_BUDGET': Value('null'), 'WORKING_SAMPLE_MIN_TOKEN_COUNT': Value('int64'), 'WORKING_SAMPLE_MAX_TOKEN_COUNT': Value('null'), 'WORKING_SAMPLE_REALIZED_TOKEN_TOTAL': Value('int64'), 'WORKING_SAMPLE_REALIZED_DOC_COUNT': Value('int64'), 'WORKING_SAMPLE_UNDERFILLED_BIN_COUNT': Value('int64'), 'WORKING_SAMPLE_COVERED_BIN_COUNT': Value('int64'), 'WORKING_SAMPLE_TOTAL_BIN_COUNT': Value('int64'), 'WORKING_SAMPLE_SAMPLING_SEED': Value('int64')}
              because column names don't match

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YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

dolma3-6t-sample-100000-docs

Materialized stratified sample of 100,000 docs per bin (~58k source-shard files, ~186 GB) from the deduplicated 6T Dolma3 corpus. Seed 42, materialized by 128 Modal workers (worker_0000/ through worker_0127/).

Layout

HCAI-Lab/dolma3-6t-sample-100000-docs/
├── bin_summary.csv
├── sample_contract.json
└── worker_NNNN/
    └── soc127__phase1_pool_shared__...__shard_NNNNNNNN.jsonl.zst (~450 files per worker)

Dual-access companion

This dataset is the dataset-API twin of the existing bucket:

Bucket: hf://buckets/HCAI-Lab/dolma3-6t-sample-100000-docs (S3-style access)
Dataset: HCAI-Lab/dolma3-6t-sample-100000-docs (this repo; snapshot_download + dataset viewer)

Both surfaces contain bit-identical data. Pick whichever access pattern fits your tooling.

Provenance

Created 2026-05-25 as part of the HCAI-Lab HF org cleanup (PR 3): every sample size now has both a bucket and a dataset twin for symmetric access. The other sample sizes (dolma3-6t-sample-{500,1000,5000,10000,50000}-docs) follow the same pattern.

Field	Value
Original bucket	`HCAI-Lab/dolma3-6t-sample-100000-docs` (created 2026-03-24)
Dataset twin created	2026-05-25
Source files	58,264 (bit-exact match to bucket)
Size	~~184 GB on HF dataset accounting, ~186.7 GB on bucket accounting (~~1.3% diff is HF-side compression variance)

See docs/data_home/inventory.json and docs/HCAI_LAB_NAMING_CONVENTION.md for the full org-wide inventory and naming rule.

Downloads last month: 15

Total file size:

184 GB

Collection including HCAI-Lab/dolma3-6t-sample-100000-docs

Dolma3 — Working Samples + Preconditioner

Collection

Stratified working samples (5 sizes, seed=42) plus 100K random preconditioner sample. • 7 items • Updated May 26