README.md

---
language:
- en
license: apache-2.0
task_categories:
- text-generation
pretty_name: StenCore — FinePDFs-Edu Curated
authors:
- StentorLabs
size_categories:
- 100K<n<1M
source_datasets:
- HuggingFaceFW/finepdfs-edu
tags:
- education
- quality-filtered
- deduplicated
- perplexity-filtered
- kenlm
- minhash
- domain-reweighted
- llm-pretraining
- english
- pii-filtered
configs:
- config_name: default
  data_files:
  - split: train
    path: data/*.parquet
---

# StenCore — FinePDFs-Edu Curated

*By [StentorLabs](https://huggingface.co/StentorLabs)*

![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)
![Language](https://img.shields.io/badge/language-English-green.svg)
![Pipeline](https://img.shields.io/badge/pipeline-StenCore%20v2026.03-orange.svg)
![Docs](https://img.shields.io/badge/documents-149k-purple.svg)
![Size](https://img.shields.io/badge/size-447%20MB-red.svg)

StenCore is StentorLabs' first dataset release — a quality-first resource built from the ground up for training language models. Derived from [HuggingFaceFW/finepdfs-edu](https://huggingface.co/datasets/HuggingFaceFW/finepdfs-edu), every document passed a 14-stage automated curation pipeline including heuristic filtering, language ID, PII redaction, toxicity screening, eval decontamination, multi-strategy deduplication, KenLM + neural perplexity scoring, and domain reweighting.

> ⚠️ **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

<details>
<summary><b>📋 Full Stage-by-Stage Breakdown</b></summary>

### 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.

</details>

---

## Adaptive Quality Thresholds

<details>
<summary><b>📐 Full learned threshold values (en:formal and en:conversational)</b></summary>

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.

</details>

---

## Deduplication Autotune

<details>
<summary><b>⚙️ Runtime autotune event log</b></summary>

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.

</details>

---

## Perplexity Scoring

<details>
<summary><b>📊 KenLM + SmolLM2 scoring details</b></summary>

### 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.


</details>

---

## Runtime & Timing

<details>
<summary><b>⏱️ Full per-stage timing table</b></summary>

| 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**).

</details>

---

## 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.

---

<p align="center">Made with ❤️ by <a href="https://huggingface.co/StentorLabs">StentorLabs</a></p>