metadata
pretty_name: docs1
language:
- en
license: cc-by-nc-nd-4.0
task_categories:
- text-generation
size_categories:
- 1M<n<10M
tags:
- long-context
- documents
- continual-learning
configs:
- config_name: default
data_files:
- split: train
path: data/*/*.parquet
docs1
The dataset of high-quality, diverse, factual documents for in-context use.
docs1 is AlphaQualia's base corpus of documents for in-context use. It contains factual, high-quality English
documents of 1K–32K tokens, sorted into 31 length buckets of 1,024 MiniCPM5-2B
tokens each. It is the shared starting point for every downstream dataset (questions, reconstructions, tool tasks,
teacher generations). Those datasets refer to documents by id and never copy them.
| Documents | 2,889,645 |
| Tokens (MiniCPM5-2B) | 34.81B |
| Length range | 1,024 – 32,768 tokens, 31 buckets |
| Docs per bucket | 25,560 (longest) to 181,521 (shortest) |
| Sources | 23 public Hub datasets |
| Exact duplicates removed | 79,462 |
| Version | v1.1 (stage 1: curation + light cleanup), built 2026-10-14 |
Usage
from datasets import load_dataset
# Everything (streams; about 34.81B tokens of text)
ds = load_dataset("alpha-qualia/docs1", split="train", streaming=True)
# One length bucket: 8,192–9,215 tokens
ds = load_dataset("alpha-qualia/docs1", data_files="data/b07_8192-9215/*.parquet", split="train")
# A length range, by exact token count
ds = ds.filter(lambda r: 8500 <= r["length_tokens_minicpm"] <= 9000)
Short documents are far more common than long ones (see the figure), so for a uniform length distribution, sample the same number of documents from every bucket folder. Every bucket has at least 25,560.
Files are stored as data/b{NN}_{low}-{high}/part-*.parquet, so a curriculum (short first, then long) can read
whole buckets. Rows are shuffled within each bucket.
Schema
| Field | Type | Description |
|---|---|---|
id |
string (UUID) | Stable document ID: UUIDv5 of source repo : source row id (plus the character span for segments). A rebuild gives the same IDs. |
created_at |
timestamp (UTC) | When the document was added to docs1. |
source_type |
string | The Hub dataset it came from (e.g. HuggingFaceFW/finepdfs-edu), or original for documents we write (later stages). |
metadata |
string (JSON) | Source fields such as url, title, license, repo, path, plus source_file. Segments add parent_id, segment, char_span. |
content |
string | The document text, unchanged except for segment cuts. |
length_tokens_minicpm |
int32 | Exact token count with the MiniCPM5-2B tokenizer (no special tokens). |
tags |
list[string] | Labels added by later stages, e.g. uhq (top-quality subset), synthetic, augmented. Empty in v1. Filter with "uhq" in r["tags"]. |
Composition
| Source | Hub dataset | Category | Docs | Tokens | Mean length | Segmented | License |
|---|---|---|---|---|---|---|---|
finepdfs-edu |
HuggingFaceFW/finepdfs-edu | PDFs | 499,065 | 7.34B | 14,704 | 16% | ODC-By 1.0 |
fineweb-edu |
HuggingFaceFW/fineweb-edu | Web | 268,881 | 3.79B | 14,095 | 14% | ODC-By 1.0 |
gutenberg |
common-pile/project_gutenberg_filtered | Books | 192,444 | 3.11B | 16,174 | 92% | open (per doc, Common Pile) |
caselaw |
common-pile/caselaw_access_project_filtered | Law | 237,961 | 2.93B | 12,323 | 6% | open (per doc, Common Pile) |
usgpo |
common-pile/usgpo_filtered | Government | 195,675 | 2.78B | 14,191 | 41% | open (per doc, Common Pile) |
pes2o |
common-pile/peS2o_filtered | Science | 218,957 | 2.35B | 10,719 | 1% | open (per doc, Common Pile) |
youtube |
common-pile/youtube_filtered | Transcripts | 205,885 | 2.23B | 10,853 | 5% | open (per doc, Common Pile) |
finewiki |
HuggingFaceFW/finewiki | Wikipedia | 187,693 | 1.87B | 9,970 | 6% | CC BY-SA 4.0 / GFDL |
stack-edu |
common-pile/stackv2_edu_filtered | Code | 176,001 | 1.81B | 10,281 | 11% | open (per doc, Common Pile) |
doab |
common-pile/doab_filtered | Books | 151,778 | 1.76B | 11,565 | 43% | open (per doc, Common Pile) |
pubmed |
common-pile/pubmed_filtered | Science | 123,865 | 1.75B | 14,145 | 5% | open (per doc, Common Pile) |
ar5iv |
marin-community/ar5iv-no-problem-markdown | Science | 126,461 | 1.21B | 9,566 | 1% | arXiv (per paper) |
stackexchange |
common-pile/stackexchange_filtered | Q&A | 95,889 | 570.6M | 5,950 | 0% | open (per doc, Common Pile) |
rules |
Andwwy/rules | Agent files | 78,808 | 558.7M | 7,089 | 4% | per repository (unverified) |
regulations |
common-pile/regulations_filtered | Government | 42,438 | 297.0M | 6,999 | 12% | open (per doc, Common Pile) |
uk-hansard |
common-pile/uk_hansard_filtered | Transcripts | 10,255 | 159.3M | 15,534 | 87% | open (per doc, Common Pile) |
skillmd |
FayeZC/SkillMD-138K | Agent files | 26,832 | 104.1M | 3,880 | 0% | per file (card: CC BY 4.0) |
pressbooks |
common-pile/pressbooks_filtered | Textbooks | 23,301 | 99.3M | 4,260 | 1% | open (per doc, Common Pile) |
libretexts |
common-pile/libretexts_filtered | Textbooks | 17,987 | 65.0M | 3,612 | 1% | open (per doc, Common Pile) |
news |
common-pile/news_filtered | News | 6,404 | 11.3M | 1,764 | 0% | open (per doc, Common Pile) |
oercommons |
common-pile/oercommons_filtered | Textbooks | 2,043 | 9.0M | 4,402 | 3% | open (per doc, Common Pile) |
peps |
common-pile/python_enhancement_proposals_filtered | Tech docs | 597 | 2.6M | 4,342 | 0% | open (per doc, Common Pile) |
public-domain-review |
common-pile/public_domain_review_filtered | Essays | 425 | 1.1M | 2,554 | 0% | open (per doc, Common Pile) |
Per-bucket counts
| Bucket (tokens) | Docs | Tokens | Largest sources |
|---|---|---|---|
| 1,024–2,047 | 181,521 | 267.2M | finepdfs-edu 11%, youtube 7%, pes2o 7% |
| 2,048–3,071 | 175,166 | 440.3M | finepdfs-edu 11%, youtube 7%, pes2o 7% |
| 3,072–4,095 | 168,259 | 596.8M | finepdfs-edu 11%, youtube 7%, pes2o 7% |
| 4,096–5,119 | 163,511 | 748.4M | finepdfs-edu 12%, youtube 7%, pes2o 7% |
| 5,120–6,143 | 155,657 | 871.9M | finepdfs-edu 12%, youtube 8%, pes2o 8% |
| 6,144–7,167 | 150,080 | 994.8M | finepdfs-edu 13%, pes2o 8%, finewiki 8% |
| 7,168–8,191 | 144,972 | 1.11B | finepdfs-edu 13%, pes2o 8%, finewiki 8% |
| 8,192–9,215 | 138,440 | 1.20B | finepdfs-edu 14%, pes2o 9%, finewiki 9% |
| 9,216–10,239 | 132,471 | 1.29B | finepdfs-edu 15%, pes2o 9%, finewiki 9% |
| 10,240–11,263 | 128,087 | 1.37B | finepdfs-edu 15%, pes2o 9%, finewiki 9% |
| 11,264–12,287 | 122,528 | 1.44B | finepdfs-edu 16%, pes2o 10%, caselaw 9% |
| 12,288–13,311 | 115,851 | 1.48B | finepdfs-edu 17%, pes2o 10%, caselaw 10% |
| 13,312–14,335 | 108,186 | 1.49B | finepdfs-edu 18%, pes2o 11%, caselaw 11% |
| 14,336–15,359 | 100,785 | 1.49B | finepdfs-edu 19%, pes2o 12%, caselaw 11% |
| 15,360–16,383 | 94,119 | 1.49B | finepdfs-edu 21%, pes2o 13%, caselaw 11% |
| 16,384–17,407 | 86,953 | 1.47B | finepdfs-edu 22%, pes2o 11%, fineweb-edu 11% |
| 17,408–18,431 | 80,582 | 1.44B | finepdfs-edu 24%, fineweb-edu 11%, caselaw 11% |
| 18,432–19,455 | 74,208 | 1.40B | finepdfs-edu 25%, fineweb-edu 12%, caselaw 10% |
| 19,456–20,479 | 68,718 | 1.37B | finepdfs-edu 26%, fineweb-edu 12%, caselaw 9% |
| 20,480–21,503 | 63,270 | 1.33B | finepdfs-edu 27%, fineweb-edu 13%, gutenberg 9% |
| 21,504–22,527 | 57,848 | 1.27B | finepdfs-edu 27%, fineweb-edu 13%, gutenberg 10% |
| 22,528–23,551 | 52,651 | 1.21B | finepdfs-edu 27%, fineweb-edu 14%, gutenberg 12% |
| 23,552–24,575 | 47,671 | 1.15B | finepdfs-edu 27%, fineweb-edu 14%, gutenberg 12% |
| 24,576–25,599 | 43,812 | 1.10B | finepdfs-edu 27%, fineweb-edu 14%, gutenberg 13% |
| 25,600–26,623 | 40,642 | 1.06B | finepdfs-edu 27%, fineweb-edu 14%, gutenberg 14% |
| 26,624–27,647 | 37,858 | 1.03B | finepdfs-edu 27%, gutenberg 15%, fineweb-edu 14% |
| 27,648–28,671 | 35,840 | 1.01B | finepdfs-edu 27%, gutenberg 16%, fineweb-edu 14% |
| 28,672–29,695 | 33,405 | 974.7M | finepdfs-edu 27%, gutenberg 17%, fineweb-edu 14% |
| 29,696–30,719 | 31,342 | 946.7M | finepdfs-edu 26%, gutenberg 18%, fineweb-edu 14% |
| 30,720–31,743 | 29,652 | 925.9M | finepdfs-edu 27%, gutenberg 18%, fineweb-edu 14% |
| 31,744–32,768 | 25,560 | 823.7M | finepdfs-edu 28%, gutenberg 19%, fineweb-edu 14% |
Construction
- Sources. Datasets that are already curated and trusted: FineWeb-Edu, FinePDFs-Edu, FineWiki, ar5iv, the openly
licensed Common Pile v0.1 subsets (papers, books, textbooks, Q&A, government,
law, transcripts, educational code), and agent instruction files (
Andwwy/rules, SkillMD). - Filters. Source-level quality filters only: FinePDFs-Edu not truncated and without near-duplicates
(
minhash_cluster_size == 1); Stack-Edu code with edu score ≥ 3; agent files mostly Latin script and with no credential patterns. - Length. Every document is tokenized with the MiniCPM5-2B tokenizer. Documents of 1,024–32,768 tokens are kept whole. Longer documents (mostly books, theses and long reports) are cut into at most 6 consecutive segments at paragraph breaks. Each segment is sized for a random length bucket that still needs documents.
- Balance. Each source collects up to a fixed number of documents per bucket (6,000; FinePDFs-Edu 12,000), so no single source can dominate a bucket. The merge removes exact duplicates and keeps every remaining document; buckets are not truncated, so balancing by length happens at load time.
- Determinism. Sources, files and rows are chosen with fixed seeds. Building again from the same Hub revisions gives the same IDs and nearly the same selection.
Cleaning (v1.1)
Conservative, rule-based, chosen by reading flagged documents from a 3% stratified sample; rules with real false positives were narrowed to the sources where they only caught junk.
| Step | Documents |
|---|---|
dropped: repeated |
2,864 |
dropped: table_dump |
2,843 |
dropped: symbol_soup |
2,254 |
dropped: non_english |
1,187 |
dropped: nav_menu |
82 |
dropped: out_of_range_after_fix |
35 |
repaired: nul |
13,553 |
repaired: mojibake |
2,236 |
| moved to another bucket after repair | 601 |
non_english: mostly non-Latin text; symbol_soup: under 60% letters, digits and spaces (prose sources); repeated: over half the text in repeated lines (PDFs and US government sources); table_dump: number tables of one or two words per line; nav_menu: scraped site navigation. nul: NUL bytes removed; mojibake: wrong-encoding text such as ’ repaired with ftfy. Repaired documents keep their id and list the repairs in metadata.cleaned. Earlier versions stay available in the repository history.
Limitations
- Long buckets are less diverse. Natural documents over ~16K tokens are rare outside PDFs, papers and books, so those categories dominate the long end. Later stages add synthetic long documents.
- Not decontaminated against any benchmark. Check overlap before using docs1 near an evaluation set.
- Novelty varies. Wikipedia and famous texts are likely known to the base model; papers, PDFs and agent files much less so.
- Mixed licenses. The repository is marked CC BY-NC-ND 4.0, but each document also keeps its source license
(see
metadata.licensewhere present; e.g. Wikipedia is CC BY-SA). Check the source license before reuse. - English only, by source selection (no language filter on Common Pile subsets beyond their own).
Roadmap
- Stage 2, original documents: newly scraped material (recent docs, repositories, papers) and model-written
documents aimed at specific length buckets (
source_type = "original"). - Stage 3, augmented documents: model-made variants of docs1 documents, from small factual edits to style changes.
Each records
metadata.parent_id.
Reproduce
# Stage 1: one CPU job per source group (8 in parallel)
uv run gpu --provider aws --region us-east-1 sagemaker submit datasets/docs1/collect.sh --instance ml.c6i.32xlarge --name aq-docs1-<group>
# Stage 2: merge, card, publish
uv run gpu --provider aws --region us-east-1 sagemaker submit datasets/docs1/merge.sh --instance ml.r6i.16xlarge --name aq-docs1-merge
Code: datasets/docs1/ in the AlphaQualia repository.