--- language: - en license: apache-2.0 task_categories: - text-generation pretty_name: StenCore — FinePDFs-Edu Curated authors: - StentorLabs size_categories: - 100K ⚠️ **Privacy & Copyright Notice:** This dataset was produced by automated pipelines that include regex-based PII redaction and heuristic filters. Automated redaction may miss personal data (false negatives) and may over-redact (false positives). No claim of complete PII removal is made. Do not use this dataset in systems that surface individual personal data. Review and clear upstream site-level licenses before redistributing or deploying models trained on this data. For takedown or privacy requests, open an issue or contact [stentorlabs@gmail.com](mailto:stentorlabs@gmail.com) with the affected doc identifier and evidence. --- ## Quick Stats | Property | Value | |---|---| | **Pipeline** | StenCore v2026.03 | | **Source** | `HuggingFaceFW/finepdfs-edu` (3 Parquet files, ~8.9 GB) | | **Docs in → out** | 584,000 → 149,000 (~37.78% keep rate) | | **Size (uncompressed)** | 447,088,827 bytes (~447.09 MB) | | **Avg doc size** | ~2,988 bytes (~750 words) | | **Synthetic docs** | 0 (100% human-authored) | | **Language** | English (`en`) | | **Platform** | Kaggle CPU | | **Total runtime** | ~9.47 hours wall / ~12+ hours CPU | --- ## Usage ```python from datasets import load_dataset ds = load_dataset("StentorLabs/stencore") # Filter to highest-quality documents top = ds["train"].filter(lambda r: r["cqf_score"] >= 0.5) print(len(top)) print(top[0]["text"][:500]) ``` --- ## Intended & Disallowed Uses **✅ Intended uses** - LLM pre-training and fine-tuning on English educational text - Quality filtering and data curation research - Domain reweighting and curriculum learning experiments **🚫 Disallowed uses** - Systems that surface or process personal information - Redistribution without reviewing upstream licensing (Apache 2.0 requires attribution; check site-level licenses for individual documents) - Any production deployment without independent legal and privacy review --- ## Pipeline Summary StenCore (`curate_2026` mode) is a fully automated curation pipeline (v2026.03) running 14 sequential stages from raw Parquet to a publish-ready HuggingFace dataset. **Every document in this dataset passed all of the following:** 1. **Columnar pre-filter** — DuckDB SQL-level filter over raw Parquet 2. **Adaptive quality thresholds** — learned from the data, not hand-tuned (per-register: formal / conversational / technical) 3. **Language ID gate** — fastText or stopword/script heuristic (`en` only) 4. **Heuristic quality filter** — word count, alpha ratio, stopword hits, punctuation ratio, repetition ratios, avg word length 5. **PII redaction** — regex detection of emails, phones, SSNs, IPs, card numbers, IBANs, API keys 6. **Toxicity screening** — lexical axis scoring, drop policy 7. **Eval decontamination** — exact, 8-gram overlap, char 3-gram, SimHash checks against benchmark sets 8. **Exact deduplication** — SHA-1 fingerprint (in-memory) 9. **MinHash near-dedup** — token shingle LSH (autotuned to 25% sample rate) 10. **CQF quality gate** — top 85% by quality score (threshold: CQF ≥ 0.4167) 11. **KenLM + neural perplexity gate** — `edugp/kenlm` Wikipedia model + SmolLM2-135M (excess mode, ref mean: 916.24) 12. **Cluster rehydration** — restores high-quality representatives from dedup-dropped clusters 13. **Domain reweighting** — per-domain weights learned from proxy model evaluation 14. **Mix optimization + final write** — domain-quota sampling, curriculum ordering, shard + merkle output
📋 Full Stage-by-Stage Breakdown ### Pre-Stage: Environment & Model Setup **KenLM Scorer** - Repo: `edugp/kenlm` | Corpus: `wikipedia/en` - Snapshot: `3fbe35c83b1a39f420a345b7c96a186c8030d834` - Mode: `first_pass` — KenLM prefilters, SmolLM2 rescores the subset **Neural Reference LM** - Model: `HuggingFaceTB/SmolLM2-135M` (torchao `int8_weight_only`) - `torch.compile`: Disabled (CPU stability) **Effective runtime profile:** ``` light_mode=False input_cap=400 bootstrap_docs=400 stage_timeout_s=90 stream_chunk=32768 ppl_batch=192 ppl_workers=1 prefix_tokens=256 kenlm_mode=first_pass ``` **12h target profile:** ``` hf_input_target_gb=7.50 hf_input_max_files=64 cqf_keep_web=0.85 prior_keep=0.90 web_ratio=0.95 strict_ratio=False dedup=memory autotune_grace=10000 ``` --- ### Stage 1: resolve_web_inputs **Wall:** 53.47s | **CPU:** 143.36s | **Ratio:** 2.68× DuckDB columnar prefilter over 3 Parquet files → `stage_columnar_prefilter.parquet`. High CPU/wall ratio confirms parallel DuckDB execution. --- ### Stage 2: adapt_multilingual_profiles **Wall:** 25.48s | **CPU:** 25.39s | **Ratio:** 1.00× Draws a pilot sample and learns per-register quality threshold parameters from the data. Three registers profiled: `en:formal` (1,854 samples), `en:conversational` (2,993 samples), `en:technical`. Single-threaded statistical pass. --- ### Stage 3: optimize_threshold_profiles **Wall:** 86.79s | **CPU:** 138.40s | **Ratio:** 1.59× Grid search over code/math profile scales, ranked by proxy quality metric. Selects best-performing threshold configuration. --- ### Stage 4: cqf_seed_verification_loop **Wall:** 0.001s | **CPU:** 0.001s Retrains CQF fastText at multiple thresholds, picks best by proxy quality. Completed near-instantly — seed was valid, no remediation needed. --- ### Stage 5: stage_web_candidate_pass **Wall:** 27,162.57s (7.5h) | **CPU:** 42,229.32s | **Ratio:** 1.55× Main filtering stage. Sequential gates per document: 1. Quick prefilter (min words / alpha) 2. HF-specific input filters (English subset, EDU v2, strip code fences) 3. Source policy (host allow/deny, ToS risk, license allowlist) 4. Line cleaner (boilerplate, nav, HTML artifacts, duplicate lines) 5. Domain routing (code / math / prose) 6. Language ID gate (+ English noise guard, code bypass) 7. Domain-aware heuristic filter (adaptive thresholds from Stage 2/3) 8. PII redaction 9. Toxicity screening 10. Eval decontamination (exact, 8-gram, char 3-gram, SimHash) 11. CQF scoring 12. Exact dedup (SHA-1, in-memory) 13. MinHash near-dedup (autotuned) 14. Semantic dedup (autotuned to `none`) Kept: 220,635 / 584,000 (37.78%). Dropped records to semantic dup pool for potential rehydration. --- ### Stage 6 (observed): mid_proxy_stage3 **Wall:** 115.50s | **CPU:** 160.80s | **Ratio:** 1.39× Intermediate proxy scoring pass over quality-filtered candidates. --- ### Stage 7: stage_web_quality_and_perplexity **Wall:** 300.05s | **CPU:** 282.49s | **Ratio:** 0.94× 1. **CQF threshold gate** — keeps top 85% (CQF ≥ 0.4167) 2. **Multi-objective property minima** — per-property floor checks 3. **Prior noise gate** — filters statistical outliers vs. quality prior 4. **Hybrid disagreement trigger** — routes CQF/secondary disagreements to full neural perplexity 5. **KenLM + SmolLM2 perplexity gate** — excess mode, ref mean 916.24 22,450 documents scored. Avg perplexity: 902.02 *(stored as excess perplexity — relative to the 916.24 calibrated mean, not absolute)*. --- ### Stage 8: rehydrate_clusters **Wall:** 40.42s | **CPU:** 40.19s | **Ratio:** 0.99× Reads the semantic dup pool. Re-adds top-quality cluster representatives (ranked by FineWeb2-like weighted formula). Optional MMR diversity selection. Single-threaded. --- ### Stage 9 (observed): mid_proxy_stage5 **Wall:** 102.92s | **CPU:** 143.10s | **Ratio:** 1.39× Second intermediate proxy scoring pass after cluster rehydration. --- ### Proxy Eval & Domain Reweighting (~60 min observed gap) Per-domain proxy scores aggregated → per-domain reweighting coefficients learned. Hundreds of source domains reweighted. Examples: | Domain | Weight | |---|---| | `apps.oregonlegislature.gov` | 1.0811 | | `fcaresources.com` | 1.0159 | | `ymcawnc.org` | 0.9999 | | `www2.cs.arizona.edu` | 0.9585 | | `echalk-slate-prod.s3.amazonaws.com` | 0.8882 | --- ### Stage 9: build_synthetic_pool **Wall:** 0.003s — No teacher LLM configured. Pool: 0 documents. ### Stage 10: stage_synthetic_filter **Wall:** 0.002s — No-op (empty pool). --- ### Stage 11: mix_optimization **Wall:** 175.34s | **CPU:** 214.87s | **Ratio:** 1.23× Proxy-evaluated search over web/synth mixing ratios. Applies domain weights from proxy eval. Produces final document selection plan. --- ### Stage 12: final_mix_and_write **Wall:** ~2,311s (~38.5 min) Domain quota enforcement → domain-weighted acceptance sampling → final exact dedup → optional curriculum ordering (easy→hard by CQF/perplexity) → streaming JSONL + Parquet write → SHA-256 + merkle root → optional deterministic sharding. | Docs Written | Bytes | Avg Bytes/Doc | |---|---|---| | 1,000 | 2,783,132 | 2,783 | | 149,000 | 447,088,827 | 2,999 | ### Stages 13–14: proxy_eval + hf_push Final proxy metric gate enforcement → auto-upload to HuggingFace Hub.
--- ## Adaptive Quality Thresholds
📐 Full learned threshold values (en:formal and en:conversational) These parameters were learned from the data in Stage 2 and optimized in Stage 3. All values are exact as logged. ### `en:formal` — 1,854 bootstrap samples | Parameter | Value | |---|---| | `min_words` | 30 | | `max_words` | 2,000 | | `min_stopwords` | 2 | | `max_line_punct_ratio` | 0.1111111111111111 | | `max_word_repeat_3gram_ratio` | 0.30466436237947997 | | `max_char_repeat_5gram_ratio` | 0.35 | | `min_alpha_ratio` | 0.5967419247419248 | | `min_avg_word_len` | 4.157040378006873 | | `max_avg_word_len` | 6.216977322149734 | ### `en:conversational` — 2,993 bootstrap samples | Parameter | Value | |---|---| | `min_words` | 18 | | `max_words` | 2,000 | | `min_stopwords` | 2 | | `max_line_punct_ratio` | 0.10838961038961101 | | `max_word_repeat_3gram_ratio` | 0.19476069102237326 | | `max_char_repeat_5gram_ratio` | 0.35 | | `min_alpha_ratio` | 0.6542321503584156 | | `min_avg_word_len` | 4.098954647914038 | | `max_avg_word_len` | 6.0 | ### `en:technical` Separate profile applied; full values truncated in logs. Expected to have higher tolerance for non-alphabetic characters (equations, code, symbols) and relaxed stopword requirements.
--- ## Deduplication Autotune
⚙️ Runtime autotune event log The autotune system fires after 10,000 documents (grace period). All 6 events occurred in a 5,000-document window — the system converges aggressively. | Event | Docs Seen | Throughput | Action | |---|---|---|---| | Start | 0 | — | `sem=hybrid`, `embed=0.0200`, `minhash=1.000` | | #1 | 10,000 | 21.81 docs/s | `embed_sample → 0.0050` | | #2 | 11,000 | 21.92 docs/s | `embed_sample → 0.0020` | | #3 | 12,000 | 22.04 docs/s | `semantic_mode → minhash` | | #4 | 13,000 | 22.13 docs/s | `minhash_sample → 0.500` | | #5 | 14,000 | 22.31 docs/s | `minhash_sample → 0.250` | | #6 | 15,000 | 22.57 docs/s | `semantic_mode → none` | | Stable | 584,000 | 21.5 docs/s | Final: `sem=none`, `embed=0.0020`, `minhash=0.250` | **Throughput gain: ~1.8×** (12.3 → 22 docs/s). MinHash at 25% sampling will miss some near-duplicate pairs — a deliberate throughput/precision tradeoff accepted by the autotune system based on observed duplicate density.
--- ## Perplexity Scoring
📊 KenLM + SmolLM2 scoring details ### Configuration - **KenLM model:** `edugp/kenlm`, `wikipedia/en`, snapshot `3fbe35c83b1a39f420a345b7c96a186c8030d834` - **Neural LM:** `HuggingFaceTB/SmolLM2-135M` (torchao int8_weight_only) - **Mode:** `first_pass` — KenLM prefilters all docs; SmolLM2 rescores near-boundary subset - **Scoring mode:** `excess` — `score = raw_perplexity - reference_mean` - **Reference mean:** 916.2404 (calibrated fresh on 256 bootstrap docs, `fit_new`) ### Scoring progress | Docs Scored | Last Doc PPL | Running Avg PPL | |---|---|---| | 50 | 7,442.60 | 218.38 | | 22,450 | 8,182.66 | 902.02 | High individual perplexity values (>7,000) are expected for math-heavy or notation-rich educational text under a Wikipedia-trained model. The `excess` mode partially normalizes this. Running mean converges to ~902 across the full scored set.
--- ## Runtime & Timing
⏱️ Full per-stage timing table | Stage | Wall Time | CPU Time | CPU/Wall | |---|---|---|---| | Model/env setup | ~167s | — | — | | `resolve_web_inputs` | 53.47s | 143.36s | 2.68× | | `adapt_multilingual_profiles` | 25.48s | 25.39s | 1.00× | | `optimize_threshold_profiles` | 86.79s | 138.40s | 1.59× | | `cqf_seed_verification_loop` | 0.001s | 0.001s | 1.00× | | `stage_web_candidate_pass` | 27,162.57s | 42,229.32s | 1.55× | | `stage_web_quality_and_perplexity` | 300.05s | 282.49s | 0.94× | | `mid_proxy_stage3` *(observed)* | 115.50s | 160.80s | 1.39× | | `rehydrate_clusters` | 40.42s | 40.19s | 0.99× | | `mid_proxy_stage5` *(observed)* | 102.92s | 143.10s | 1.39× | | Proxy / domain reweighting | ~3,590s | — | — | | `build_synthetic_pool` | 0.003s | 0.005s | — | | `stage_synthetic_filter` | 0.002s | 0.002s | — | | `mix_optimization` | 175.34s | 214.87s | 1.23× | | `final_mix_and_write` | ~2,311s | — | — | | **TOTAL** | **~34,088s** | **~43,377s+** | **~1.27×** | Candidate pass throughput: ~12.3 docs/s (initial) → ~22 docs/s (post-autotune, **~1.8× gain**).
--- ## Limitations - **PDF extraction artifacts** — OCR artifacts, broken equations, and malformed tables may be present despite filtering. - **Residual PII** — Automated regex redaction does not guarantee complete PII removal. Do not use for systems that surface personal information. - **Copyright** — Source PDFs may carry individual site-level licenses. Apache 2.0 requires attribution; verify upstream licensing for your use case. - **KenLM Wikipedia bias** — Math-heavy or highly technical documents may be underrepresented due to high perplexity under a Wikipedia-trained model. - **~62% rejection rate** — Some valid educational content may have been dropped due to heuristic threshold mismatch (e.g., table-heavy or equation-dense formatting). - **English only** — Pipeline profiled `en:formal`, `en:conversational`, and `en:technical` registers only. - **No synthetic data** — This run did not use the synthetic generation system. Dataset is 100% source text. - **MinHash 25% sampling** — Post-autotune dedup will miss some near-duplicate pairs. --- ## Licensing & Citation Released under [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0). Attribution required. Derived from [HuggingFaceFW/finepdfs-edu](https://huggingface.co/datasets/HuggingFaceFW/finepdfs-edu) — review upstream licensing before use. ```bibtex @dataset{stencore_finepdfs_edu_curated, title = {StenCore: FinePDFs-Edu Curated}, author = {StentorLabs}, year = {2026}, note = {StentorLabs' first dataset. StenCore pipeline v2026.03. 584k docs in, 149k out. Adaptive heuristics, PII redaction, toxicity/decontam gates, MinHash + KenLM + neural perplexity, CQF scoring, proxy domain reweighting.}, howpublished = {\url{https://huggingface.co/datasets/StentorLabs/stencore}} } @dataset{fineweb_finepdfs_edu, author = {HuggingFace FineWeb Team}, title = {FinePDFs-Edu}, howpublished = {\url{https://huggingface.co/datasets/HuggingFaceFW/finepdfs-edu}} } ``` **Contact:** [StentorLabs@gmail.com](mailto:StentorLabs@gmail.com) — for takedown requests, privacy concerns, or feedback. ---

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