The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: TypeError
Message: Couldn't cast array of type
struct<fineweb_edu_train_00001_00000_tokens.bin: struct<indexed_documents: int64, indexed_tokens: int64, sha256: string, size_bytes: int64>, fineweb_edu_train_00001_00000_tokens.idx: struct<sha256: string, size_bytes: int64>>
to
{'fineweb_edu_train_00000_00000_tokens.bin': {'indexed_documents': Value('int64'), 'indexed_tokens': Value('int64'), 'sha256': Value('string'), 'size_bytes': Value('int64')}, 'fineweb_edu_train_00000_00000_tokens.idx': {'sha256': Value('string'), 'size_bytes': Value('int64')}}
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
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 129, 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 489, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/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/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_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 2303, in cast_table_to_schema
cast_array_to_feature(
~~~~~~~~~~~~~~~~~~~~~^
table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
feature,
^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1852, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
~~~~^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2149, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
TypeError: Couldn't cast array of type
struct<fineweb_edu_train_00001_00000_tokens.bin: struct<indexed_documents: int64, indexed_tokens: int64, sha256: string, size_bytes: int64>, fineweb_edu_train_00001_00000_tokens.idx: struct<sha256: string, size_bytes: int64>>
to
{'fineweb_edu_train_00000_00000_tokens.bin': {'indexed_documents': Value('int64'), 'indexed_tokens': Value('int64'), 'sha256': Value('string'), 'size_bytes': Value('int64')}, 'fineweb_edu_train_00000_00000_tokens.idx': {'sha256': Value('string'), 'size_bytes': Value('int64')}}Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
FineWeb-Edu GPT-2 Megatron
This repository contains a deterministic, pretokenized Megatron indexed dataset built from a pinned revision of the globally shuffled FineWeb-Edu 100BT subset.
Important split-overlap disclosure
The split source files are disjoint, but their text is not content-deduplicated across splits. The artifact is published unchanged with the measured overlap preserved:
| Held-out split | Exact matches with training | MinHash candidate overlap with training |
|---|---|---|
| development | 375 documents (4.9349%); 362,367 IDs (4.5294%) | 598 documents (7.8695%); 639,308 IDs (7.9911%) |
| final | 4,909 documents (5.0697%); 4,743,920 IDs (4.7439%) | 7,873 documents (8.1307%); 8,491,487 IDs (8.4914%) |
Exact matches were confirmed with SHA-256 and raw UTF-8 equality. MinHash results are candidate clusters, not confirmed near duplicates. Do not describe the development or final split as contamination-free.
This behavior is consistent with the upstream dataset: FineWeb-Edu inherits FineWeb's independent per-CommonCrawl-dump deduplication, while the source used here was subsequently shuffled globally.
Contents
| Split | Prefix | Documents | Available indexed IDs | UTF-8 source bytes |
|---|---|---|---|---|
| training | data/train |
7,290,286 | 7,556,553,510 | 35,173,581,642 |
| development | data/development |
7,599 | 8,000,266 | 37,310,247 |
| final | data/final |
96,830 | 100,001,217 | 465,855,889 |
The training artifact contains complete source shards 0 through 7. The available indexed-ID count is the physical capacity of those selected shards; downstream users choose their own consumption budget.
Each nonempty source document was encoded independently with the pinned GPT-2
tokenizer and followed by exactly one EOD ID 50,256. No external normalization,
implicit BOS, or other implicit special token was applied. Token IDs are stored
as uint16.
Pinned inputs
| Item | Revision |
|---|---|
| Source | HuggingFaceFW/fineweb_edu_100BT-shuffled@be6b2a50d3a9c60d330c45384e80c7863cd3a25d |
| Tokenizer | openai-community/gpt2@607a30d783dfa663caf39e06633721c8d4cfcd7e |
| DataTrove | 87f7bad5c4a56ec648265fbf0b91d7d226bad428 |
| Megatron-LM / MCore | 571370c829ca768fe37244f4e2e7f28d8accc4ab |
The tokenizer vocabulary size is 50,257. The pinned tokenizer.json SHA-256
is 8414cab924d8b9b33013f0d221c5862f365ee9be39c5c2bfae8a5a9e970478a6.
Environment and source-file identities are recorded under provenance/ and in
artifact-manifest.json.
Loading with pinned MCore
Use independent prefixes:
--train-data-path /path/to/fineweb-edu-gpt2-megatron/data/train
--valid-data-path /path/to/fineweb-edu-gpt2-megatron/data/development
--test-data-path /path/to/fineweb-edu-gpt2-megatron/data/final
--tokenizer-type NullTokenizer
--vocab-size 50257
--null-tokenizer-eod-id 50256
--null-tokenizer-pad-id -1
Do not combine these independent split prefixes with --split or
--data-path.
The artifact was verified with pinned MCore as a memory-mapped uint16
IndexedDataset. Document boundaries are contiguous and the first and last
document of every split end in EOD 50,256.
Integrity
| File | SHA-256 |
|---|---|
data/train.bin |
94c1cd2266a162a615d27b6b11ac1c1cc5887d629742eefad8efff1976b46222 |
data/train.idx |
564815271149b0322bd262b9f681e3feb30a96d7c677162ad5ef9f2ee3458614 |
data/development.bin |
b806f2f1ba34e8d86c3f6517f9aa305fa554d850c44362790bf6b84b88f47d55 |
data/development.idx |
aac74ffcf4d090a3bc96c320227a25805f3b0666f4595e36ac6f98a0c828d713 |
data/final.bin |
9fb1f3d21deabe8b9cc85675b21cebb7434f1221ea8cc8ebb825069729e97a49 |
data/final.idx |
efca13527d24b44423fc521947ce8c630b6739e15f5d8139ad84d9efc56be85d |
artifact-manifest.json records every uploaded payload file's size and
SHA-256, plus a deterministic tree hash.
Audits
The exhaustive exact audit scanned all selected training documents and the complete held-out prefixes. It found zero SHA-1-64 collision candidates after SHA-256 and raw-byte confirmation.
The full MinHash audit covered 7,394,715 documents, 7,664,554,993 indexed IDs,
and 35,676,747,778 UTF-8 bytes. Every document received a 5-gram signature.
The configuration was English spaCy tokenization, 5-grams, 14 buckets, 8
hashes per bucket, SHA-1 at 64-bit precision, seed 1, and four CPU workers.
It reported 8,439 training-to-held-out candidate clusters containing 19,640
documents. The retained audit tree's SHA-256 is
d550804eb3869d5c99cf3466383264088cf0a6b9ac92e7377012f1056a49d92b.
Compact reports and candidate metadata are under audits/. No filtering,
removal, or backfill step was applied.
License
The source dataset is released under ODC-By. This derived tokenized artifact retains that license.
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