geodesic-research/control-pretraining-ai-discourse

Auto-generated by dataset-builder. Each config below is a separate dataset produced from a versioned YAML build config. Load with:

from datasets import load_dataset

ds = load_dataset("geodesic-research/control-pretraining-ai-discourse", "<config_name>", revision="<commit-sha>")

Pin revision= to the specific commit SHA you want; without it, you get the current HEAD of the dataset repo, which may change when the builder re-pushes.

Requires datasets v4+. These parquet files carry feature metadata written by datasets 4.x, which uses type names ({"_type": "List"}) that 3.x does not recognise — a 3.x load_dataset fails with a bare must be called with a dataclass type or instance, naming neither the file nor the cause. The Arrow data itself is fine, so a consumer stuck on 3.x can read the parquet through Arrow directly and let the schema be inferred — but inference recovers the values, not always the declared features (an all-empty list column infers as null), so check the columns you care about rather than assuming a clean round-trip.

What this dataset holds

AI and machine-learning discourse from the control-pretraining corpora, and the documents two safety filters took out of a model's training data. Every config is one train split, and every document carries id, source, text and num_tokens (the Nemotron base tokenizer's count of text, without an end-of-document token).

config	what it holds
climbmix_ai_docs	NVIDIA ClimbMix's documents selected by an AI-discourse regex gate, each with the rule (flag_category) and the text (flag_term) that selected it
zyda_ai_docs	the same gate over Zyda-2's 100B-token sample
nemotron_wiki_rewrite_ai_docs	the same gate over the Wikipedia rewrite of NVIDIA's Nemotron pretraining data
arxiv_papers	arXiv's cs.AI, cs.CV, cs.LG and stat.ML papers as their own LaTeX, bibliography and preamble removed
ai_safety_and_adjacent	LessWrong with the Alignment Forum, the EA Forum, the Stampy and ARD articles, rewrites of LessWrong posts, and AI labs' risk reports
filtered_broad	every document the broad filter took out of the Broadly Filtered model's pretraining and midtraining data
filtered_narrow	every document the end-to-end narrow filter took out of the Narrowly Filtered V2 E2E model's pretraining and midtraining data

The first five are copied unchanged from geodesic-research/control-pretraining-datasets at revision b367be41, so their ids join back to it.

The filters. The broad filter removes a document when a canary check flags it or the GPT-5 Mini judge scores it 2 or more. The narrow filter removes a document when a canary check flags it or the GPT-5.5 judge scores it 4 or more, and the V2 E2E model applied it to both training stages. Each filtered set stacks the filtered side of every corpus its model trained on and keeps one row per distinct text (by SHA-256). A document's provenance lists every filtered split its text was found in, as {stage, corpus, split}, and its id, source and num_tokens are those of its first occurrence. A text filtered from one copy can still have been trained on in another copy the filter kept; those texts are included, so each set is everything its filter took out.

Composition — documents and Nemotron tokens per config

Computed from the published data on every card regeneration. Totals are column sums per config, and configs share documents: the filtered sets draw on the corpora the other configs come from, so summing across rows overstates unique volume.

subset	documents	total tokens	mean	median	p95	max
ai_safety_and_adjacent	352,949	658,148,626	1,865	1,042	5,478	1,304,786
arxiv_papers	433,714	8,000,009,008	18,445	14,118	42,035	37,014,723
climbmix_ai_docs	13,506,352	15,892,372,146	1,177	659	3,188	988,937
filtered_broad	577,689	3,034,476,419	5,253	963	23,984	2,799,888
filtered_narrow	50,123	338,011,332	6,744	1,101	29,266	1,883,344
nemotron_wiki_rewrite_ai_docs	53,041	188,829,967	3,560	1,549	12,983	982,969
zyda_ai_docs	1,536,755	4,550,102,536	2,961	1,119	11,727	450,565

Configs

Config	Source	Transform	Splits
ai_safety_and_adjacent	geodesic-research/control-pretraining-datasets	none	none
arxiv_papers	geodesic-research/control-pretraining-datasets	none	none
climbmix_ai_docs	geodesic-research/control-pretraining-datasets	none	none
filtered_broad	geodesic-research/control-pretraining-datasets	hf/stack_subsets → map_column → aggregate → project	none
filtered_narrow	geodesic-research/control-pretraining-datasets	hf/stack_subsets → hf/stack_subsets → map_column → aggregate → project	none
nemotron_wiki_rewrite_ai_docs	geodesic-research/control-pretraining-datasets	none	none
zyda_ai_docs	geodesic-research/control-pretraining-datasets	none	none

Provenance

ai_safety_and_adjacent

Source: geodesic-research/control-pretraining-datasets

python -m dataset_builder configs/ai_safety_and_adjacent.yaml --push

arxiv_papers

Source: geodesic-research/control-pretraining-datasets

python -m dataset_builder configs/arxiv_papers.yaml --push

climbmix_ai_docs

Source: geodesic-research/control-pretraining-datasets

python -m dataset_builder configs/climbmix_ai_docs.yaml --push

filtered_broad

Source: geodesic-research/control-pretraining-datasets Transform: hf/stack_subsets → map_column → aggregate → project

python -m dataset_builder configs/filtered_broad.yaml --push

filtered_narrow

Source: geodesic-research/control-pretraining-datasets Transform: hf/stack_subsets → hf/stack_subsets → map_column → aggregate → project

python -m dataset_builder configs/filtered_narrow.yaml --push

nemotron_wiki_rewrite_ai_docs

Source: geodesic-research/control-pretraining-datasets

python -m dataset_builder configs/nemotron_wiki_rewrite_ai_docs.yaml --push

zyda_ai_docs

Source: geodesic-research/control-pretraining-datasets

python -m dataset_builder configs/zyda_ai_docs.yaml --push

Reproducibility

All splits use split_hash() (MD5-based, seeded) so rebuilding from the same config against the same source data produces identical partitions. For an LLM-generated dataset, a provider's seed parameter is best-effort; pin consumer loads to a specific HF commit SHA to avoid drift when the builder re-pushes.

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