Olmo 3 Pre-training
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All artifacts related to Olmo 3 pre-training
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Dataset Viewer
The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code: FeaturesError
Exception: ArrowInvalid
Message: Schema at index 1 was different:
id: string
metadata: struct<cc_dump: string, dolma2_qc: struct<0: double, 1: double>, exact_duplicates: int64, lang: struct<en: double>, madlad: struct<num_sentences: int64, rule.2: list<item: int64>, rule.5: list<item: int64>, status: string>, minhash: null, original_word_count: int64, sa_remove_ranges: list<item: null>, text_hash: string, warc_content_type: string, warc_date: string, warc_url: string, weborganizer: struct<__label__adult_content: double, __label__crime_and_law: double, __label__entertainment: double, __label__finance_and_business: double, __label__games: double, __label__home_and_hobbies: double, __label__science_math_and_technology: double, __label__social_life: double, __label__software: double, __label__software_development: double, __label__art_and_design: double, __label__education_and_jobs: double, __label__fashion_and_beauty: double, __label__health: double, __label__literature: double, __label__sports_and_fitness: double>, weborganizer_max: string>
text: string
vs
id: string
metadata: struct<cc_dump: string, dolma2_qc: struct<0: double, 1: double>, exact_duplicates: int64, lang: struct<en: double>, madlad: struct<num_sentences: int64, rule.2: list<item: int64>, rule.5: list<item: int64>, status: string>, minhash: struct<cc_id: int64, cc_idx: int64, cc_size: int64>, original_word_count: int64, sa_remove_ranges: list<item: list<item: int64>>, text_hash: string, warc_content_type: string, warc_date: string, warc_url: string, weborganizer: struct<__label__adult_content: double, __label__art_and_design: double, __label__crime_and_law: double, __label__entertainment: double, __label__fashion_and_beauty: double, __label__food_and_dining: double, __label__games: double, __label__health: double, __label__home_and_hobbies: double, __label__social_life: double, __label__electronics_and_hardare: double, __label__literature: double, __label__sports_and_fitness: double, __label__education_and_jobs: double, __label__finance_and_business: double, __label__industrial: double, __label__software: double, __label__science_math_and_technology: double, __label__transportation: double, __label__software_development: double, __label__travel_and_tourism: double, __label__religion: double, __label__history_and_geography: double, __label__politics: double>, weborganizer_max: string>
text: string
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 246, 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 3496, 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 2257, in _head
return next(iter(self.iter(batch_size=n)))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2461, in iter
for key, example in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1952, in __iter__
for key, pa_table in self._iter_arrow():
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1974, in _iter_arrow
yield from self.ex_iterable._iter_arrow()
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 547, in _iter_arrow
yield new_key, pa.Table.from_batches(chunks_buffer)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "pyarrow/table.pxi", line 5039, in pyarrow.lib.Table.from_batches
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: Schema at index 1 was different:
id: string
metadata: struct<cc_dump: string, dolma2_qc: struct<0: double, 1: double>, exact_duplicates: int64, lang: struct<en: double>, madlad: struct<num_sentences: int64, rule.2: list<item: int64>, rule.5: list<item: int64>, status: string>, minhash: null, original_word_count: int64, sa_remove_ranges: list<item: null>, text_hash: string, warc_content_type: string, warc_date: string, warc_url: string, weborganizer: struct<__label__adult_content: double, __label__crime_and_law: double, __label__entertainment: double, __label__finance_and_business: double, __label__games: double, __label__home_and_hobbies: double, __label__science_math_and_technology: double, __label__social_life: double, __label__software: double, __label__software_development: double, __label__art_and_design: double, __label__education_and_jobs: double, __label__fashion_and_beauty: double, __label__health: double, __label__literature: double, __label__sports_and_fitness: double>, weborganizer_max: string>
text: string
vs
id: string
metadata: struct<cc_dump: string, dolma2_qc: struct<0: double, 1: double>, exact_duplicates: int64, lang: struct<en: double>, madlad: struct<num_sentences: int64, rule.2: list<item: int64>, rule.5: list<item: int64>, status: string>, minhash: struct<cc_id: int64, cc_idx: int64, cc_size: int64>, original_word_count: int64, sa_remove_ranges: list<item: list<item: int64>>, text_hash: string, warc_content_type: string, warc_date: string, warc_url: string, weborganizer: struct<__label__adult_content: double, __label__art_and_design: double, __label__crime_and_law: double, __label__entertainment: double, __label__fashion_and_beauty: double, __label__food_and_dining: double, __label__games: double, __label__health: double, __label__home_and_hobbies: double, __label__social_life: double, __label__electronics_and_hardare: double, __label__literature: double, __label__sports_and_fitness: double, __label__education_and_jobs: double, __label__finance_and_business: double, __label__industrial: double, __label__software: double, __label__science_math_and_technology: double, __label__transportation: double, __label__software_development: double, __label__travel_and_tourism: double, __label__religion: double, __label__history_and_geography: double, __label__politics: double>, weborganizer_max: string>
text: stringNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Dolma 3 Sample: 150B Mix
Dataset Sources
Sample of data for 1Bx5C and 7Bx1B. For the full Dolma 3 pool, see: https://huggingface.co/datasets/allenai/dolma3
| Name | Tokens | Documents | License |
|---|---|---|---|
| Common Crawl | 118.8B | 9.67B | ODC-BY |
| olmOCR Science PDFs | 18.2B | 101M | ODC-BY |
| StackEdu (Rebalanced) | 11.0B | 167M | ODC-BY |
| FineMath 3+ | 4.0B | 21.4M | ODC-BY |
| arXiv | 1.3B | 3.95M | ODC-BY |
| Wikipedia & Wikibooks | 63.5M | 6.67M | ODC-BY |
| Total | 153.3B | 10.0B |
Licensing Information
Dolma 3 is licensed under the Open Data Commons Attribution License v1.0 (ODC-By). It is intended for research and educational use. For more information, please see our Responsible Use Guidelines.
Citation
A technical manuscript is forthcoming!
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