Add dataset card

README.md

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+---
+language:
+- en
+- zh
+- ko
+license: other
+license_name: common-crawl-terms-of-use
+license_link: https://commoncrawl.org/terms-of-use
+tags:
+- common-crawl
+- pretraining
+- sft
+- dpo
+- instruction-tuning
+- preference-data
+- web-text
+- llm-data-pipeline
+task_categories:
+- text-generation
+- summarization
+size_categories:
+- 1K<n<10K
+pretty_name: LLM Data Processing — Common Crawl to Pretrain/SFT/DPO Pipeline
+---
+
+# LLM Data Processing
+
+Real, end-to-end LLM data pipeline output: raw Common Crawl WARC records,
+progressively filtered/cleaned into a pretraining corpus, plus a derived SFT
+(instruction-tuning) set and a DPO (preference) set. Every file here is the
+actual output of a script run — no synthetic placeholders — against a real
+Common Crawl batch (`CC-MAIN-2026-25`, discovered dynamically at run time,
+not hardcoded).
+
+**Source code, full run logs, and the research/decision notes behind every
+processing choice are on GitHub:**
+[mkd-minju/LLM-Data-Processing](https://github.com/mkd-minju/LLM-Data-Processing)
+— see `Task2 Phase A/B/C/D` for the scripts that produced every file below,
+and each `research_and_improve*.md` for why each design decision was made.
+
+> **This dataset is private and should stay that way (or be re-reviewed
+> before ever going public).** It is real scraped web content: PII masking
+> is regex + NER based and known to be incomplete, and the underlying page
+> text is not rights-cleared for redistribution (see **Licensing** below).
+
+## Dataset summary
+
+| Stage | File(s) | Rows | Size | What it is |
+|---|---|---|---|---|
+| Pretraining — raw | `Task2 Phase A/raw_documents.jsonl` | 3,000 | 391.3 MB | Verbatim HTML streamed from a real WARC file, `text/html` responses only |
+| Pretraining — extracted | `Task2 Phase A/extracted_documents.jsonl` | 993 | 5.9 MB | Boilerplate-stripped body text, English-only (langid-filtered) |
+| Pretraining — filtered | `Task2 Phase A/filtered_documents.jsonl` | 629 | 2.48 MB | + Gopher/C4 quality filter, exact-hash dedup, MinHash LSH near-dup removal (v1 config) |
+| Pretraining — filtered (v2) | `Task2 Phase A/filtered_documents_v2.jsonl` | 630 | 2.48 MB | Same as above but with Dolma/DataTrove-calibrated MinHash band params + post-hoc Jaccard cutoff |
+| Pretraining — final | `Task2 Phase A/cleaned_documents.jsonl` | 629 | 2.40 MB | `filtered_documents.jsonl` + PII masking (emails/phones/IPs/names → `[EMAIL]`/`[PHONE]`/`[IP]`/`[NAME]`) — **this is the pretraining-ready corpus** |
+| Tokenizer | `Task2 Phase A/bpe_tokenizer.json` | — | 486 KB | BPE tokenizer trained from scratch on `cleaned_documents.jsonl`, vocab_size=8000 |
+| SFT — raw | `Task2 Phase B/sft_raw.jsonl` | 80 | 266 KB | prompt/answer pairs before ChatML formatting |
+| SFT — final | `Task2 Phase B/sft_dataset.jsonl` | 80 | 580 KB | ChatML-formatted, near-duplicate-prompt filtered, length/relevance quality-gated — **SFT-training-ready** |
+| DPO — raw | `Task2 Phase C/dpo_pairs.jsonl` | 15 | 70 KB | concise/elaborate answer pairs with a chosen/rejected judgment |
+| DPO — final | `Task2 Phase C/dpo_pairs_filtered.jsonl` | 10 | 44 KB | pairs kept after two independent judges (grounding vs. conciseness) agreed on the chosen side — **DPO-training-ready** |
+
+Supporting/intermediate files (`Task2 Phase B/answers_*.json`,
+`sampled_80.json`, `Task2 Phase C/dpo_group*.json`, `judge_*.json`,
+`picked_15_prompts.json`, `dpo_blind_pairs.json`) are the intermediate
+artifacts these final files were assembled from — kept for traceability,
+not intended for direct training use.
+
+## Dataset creation / methodology
+
+**Pretraining corpus (Phase A).** A real ~940 MB WARC file was streamed
+(not fully downloaded) from the latest Common Crawl batch until 3,000
+`text/html` response records were collected (≈39% of the file). Of those:
+66.9% were dropped as non-English (fastText langid), body text was extracted
+with `trafilatura` (a 96.4% size reduction vs. raw HTML — most of a raw page
+is markup/boilerplate, not content), encoding was repaired with `ftfy`,
+Gopher/C4-style heuristics + exact-hash + MinHash-LSH near-dup detection
+removed low-quality/duplicate documents, and PII was masked with regex
+(email/phone/IP) + spaCy NER (`PERSON`). Net funnel: **3,000 → 629 documents
+kept (79% dropped)**. A from-scratch BPE tokenizer (vocab_size=8000) reached
+98.0% vocabulary utilization on the final corpus.
+
+**SFT set (Phase B).** 80 documents were sampled from the Phase A corpus and
+assigned one of 5 instruction types (summarize / key-signal / audience /
+counter-perspective / follow-up-question). Answers were written by an LLM
+(Claude) actually reading each excerpt — not template-extracted. Prompts were
+deduplicated by embedding similarity (`sentence-transformers`,
+cosine ≥ 0.85 → drop) and answers were quality-gated (≥8 words, ≥2 keyword
+overlap with the source). **Result: 80/80 passed every filter** — a genuine
+finding, not a bug: an earlier template-based generator lost 28.5% of samples
+to the same filters, and that failure mode disappeared once answers came
+from an LLM that actually read the source (see
+`Task2 Phase B/research_and_improve_B.md`).
+
+**DPO set (Phase C).** 15 prompts got two answer variants each (concise vs.
+elaborate), each pair judged chosen/rejected against a written rubric. To
+test judgment reliability, the same 15 blind pairs were independently judged
+twice more under two different explicit criteria (groundedness vs.
+conciseness). **The two criteria agreed on only 10/15 pairs (66.7%)** —
+disagreeing pairs were dropped, leaving the 10 in `dpo_pairs_filtered.jsonl`.
+This reproduces the annotator-disagreement problem described in
+*"Preference Consistency Matters"* (arXiv:2408.12799).
+
+**Evaluation (Phase D, not included as data here).** 5 self-authored
+GSM8K-style problems were checked against the full A/B/C corpus for 8-gram
+overlap (0 contamination hits, as expected — self-authored). A toxic-term
+denylist scan of Phase B/C found 1 real hit (a casual "shit" in a Star Wars
+fan-blog excerpt that made it through Phase A) — confirming a toxicity
+filter is not optional once source text is real crawled web content.
+
+## Data fields
+
+**`raw_documents.jsonl`** (pretraining, raw)
+```json
+{"url": "...", "html": "<raw HTML string, original encoding>", "warc_date": "2026-06-05T22:41:22Z", "content_length": 17129}
+```
+
+**`extracted_documents.jsonl` / `filtered_documents.jsonl` / `filtered_documents_v2.jsonl`** (pretraining, intermediate)
+```json
+{"url": "...", "warc_date": "...", "text": "extracted body text", "language": "en", "language_score": 0.345, "html_length": 19085, "extracted_length": 1318}
+```
+
+**`cleaned_documents.jsonl`** (pretraining, final — PII masked)
+```json
+{"url": "...", "warc_date": "...", "text": "...names/emails/phones/IPs replaced with [NAME]/[EMAIL]/[PHONE]/[IP]...", "language": "en"}
+```
+
+**`sft_dataset.jsonl`** (SFT, final)
+```json
+{
+  "index": 0, "instruction_type": "summarize",
+  "prompt": "다음 글을 1~2문장으로 요약해줘:\n\n...",
+  "answer": "...",
+  "source_url": "...", "source_excerpt": "...",
+  "messages": [{"role": "system", "content": "..."}, {"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}],
+  "text": "<|im_start|>system\n...<|im_end|>\n<|im_start|>user\n...<|im_end|>\n<|im_start|>assistant\n...<|im_end|>"
+}
+```
+
+**`dpo_pairs_filtered.jsonl`** (DPO, final)
+```json
+{
+  "dpo_id": 1, "prompt": "...",
+  "chosen": "...", "rejected": "...",
+  "chosen_side": "elaborate",
+  "justification": "why the judge picked this side",
+  "source_url": "...", "source_excerpt": "..."
+}
+```
+
+## Languages
+
+Predominantly English (the corpus is filtered to `language == "en"` via
+fastText). Instruction prompts/system text in Phase B/C are Korean;
+answers/model text are English. Note the langid model is top-1 with **no
+confidence threshold** — e.g. one retained "English" document in
+`extracted_documents.jsonl` is actually Chinese-language content with a
+`language_score` of only 0.345, misclassified because nothing filters on
+score. Treat `language_score` as a real, usable quality signal the current
+pipeline does not yet act on.
+
+## Known limitations
+
+- **PII masking is not exhaustive.** Regex covers common email/phone/IP
+  formats; name masking relies on spaCy `en_core_web_sm` NER, which has
+  real false positives (brand/product names tagged as `PERSON`) and, being
+  a statistical model, real false negatives too. Do not assume this corpus
+  is PII-free.
+- **Copyright / licensing.** `raw_documents.jsonl` through
+  `cleaned_documents.jsonl` contain (masked, but otherwise verbatim-derived)
+  text scraped from live third-party websites. Common Crawl's own crawl
+  index/metadata is available under their terms, but the underlying page
+  content's copyright remains with the original site owners — this is not
+  a "free to redistribute" text corpus. Treat as research/educational use
+  only.
+- **Scale.** Hundreds to low-thousands of rows per split, single-process —
+  a POC-scale reproduction of production logic (DataTrove/Dolma-equivalent
+  filters), not a production-scale corpus.
+- **No rejection sampling.** SFT/DPO answers are one generated candidate
+  per prompt (Phase B) or two (Phase C), not Llama-3-style multi-candidate
+  generation + reward-model/human selection. At this sample size that gap
+  hasn't visibly hurt quality, but it's a structural difference from
+  production post-training pipelines.
+- **Preference labels are noisy.** The 66.7% inter-criterion agreement in
+  Phase C means `chosen`/`rejected` reflects one plausible judgment, not a
+  ground truth — expect similar disagreement rates if you re-judge these
+  pairs independently.
+
+## How to load
+
+```python
+from datasets import load_dataset
+
+pretrain = load_dataset("json", data_files="Task2 Phase A/cleaned_documents.jsonl")
+sft = load_dataset("json", data_files="Task2 Phase B/sft_dataset.jsonl")
+dpo = load_dataset("json", data_files="Task2 Phase C/dpo_pairs_filtered.jsonl")
+```
+
+## Licensing
+
+No explicit redistribution license is granted for the raw web text itself
+(see **Known limitations** above). This dataset is shared for research/
+educational purposes documenting a data-pipeline methodology, not as a
+licensed corpus for downstream commercial use. Common Crawl's Terms of Use:
+https://commoncrawl.org/terms-of-use