{
  "$type": "site.standard.document",
  "bskyPostRef": {
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    "uri": "at://did:plc:pgryn3ephfd2xgft23qokfzt/app.bsky.feed.post/3mnz6ubtjo6t2"
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  "path": "/t/how-can-i-build-a-high-quality-dataset/176571#post_9",
  "publishedAt": "2026-06-11T10:27:40.000Z",
  "site": "https://discuss.huggingface.co",
  "tags": [
    "Lost in the Middle",
    "Instruction-following robustness / prompt injection",
    "Instruction-following survey",
    "Long-context instruction following",
    "RedPajama",
    "FineWeb",
    "FinerWeb-10BT",
    "Hazm",
    "Hazm GitHub",
    "PersianTools",
    "Lucene PersianNormalizer",
    "Joint Persian Word Segmentation Correction and ZWNJ Recognition"
  ],
  "textContent": "Hmm, after looking into it, it seems to be something like this:\n\n* * *\n\n## Short answer\n\nI would separate the two questions.\n\nFor the first question:\n\n> Is it true that only larger models can maintain attention and follow noisy/long instructions reliably?\n\nMostly yes, in practice. Larger models usually handle messy, long, multi-constraint prompts better. But I would not phrase it as “only large models can do it.” A 0.8B model can still be useful if the product design reduces the burden on the model.\n\nFor the second question:\n\n> Should the n-gram model handle noisy Wikipedia tails, or should I clean them first?\n\nClean them first.\n\nThe n-gram model should be a **quality scorer** , not a **garbage collector**.\n\nIf the text has good Persian prose followed by reference/citation garbage, that is usually a **boundary/truncation problem** , not necessarily a reason to reject the whole document.\n\n* * *\n\n## 1. About 0.8B models and noisy prompts\n\nYour intuition is reasonable.\n\nA 0.8B model should not be expected to handle the same prompt complexity as a strong 7B, 8B, 14B, or frontier model.\n\nThe problem is not only Persian. Even much larger models can fail when prompts are:\n\n  * long\n  * messy\n  * ambiguous\n  * internally contradictory\n  * full of irrelevant context\n  * full of embedded instructions\n  * multi-step\n  * multi-constraint\n  * noisy or poorly formatted\n\n\n\nThis is related to several known issues:\n\n  * Lost in the Middle: models may fail to use information reliably when it appears in the middle of long contexts.\n  * Instruction-following robustness / prompt injection: models may struggle to distinguish which instructions to follow and which to ignore.\n  * Instruction-following survey: instruction following is a broad and still nontrivial problem.\n  * Long-context instruction following: longer context windows do not automatically solve instruction adherence.\n\n\n\nSo I would agree with your concern:\n\n> If an 8B model struggles with noisy, unclear, long prompts, a 0.8B model will probably struggle more.\n\nBut the practical answer is not simply “give up.” The answer is:\n\n> Do not design the system so that the 0.8B model has to solve everything inside one messy prompt.\n\n* * *\n\n## 2. Reduce the burden on the 0.8B model\n\nFor a small model, the system design matters a lot.\n\nInstead of asking the model to handle this:\n\n\n    long noisy user prompt\n    + mixed task\n    + unclear context\n    + multiple instructions\n    + irrelevant text\n    + long document\n    + expected structured answer\n\n\ntry to convert it into this:\n\n\n    short clean task\n    + one clear instruction\n    + limited relevant context\n    + simple expected format\n\n\nA 0.8B assistant can become much more useful if you do preprocessing before the prompt reaches the model.\n\n### Practical design\n\nProblem | Better design\n---|---\nUser gives long messy text | clean/split/summarize before model call\nUser asks multiple things | split into subtasks\nPrompt contains irrelevant context | retrieve/select only relevant spans\nPrompt is unclear | ask a clarification question\nPrompt has many constraints | use a template with explicit fields\nLong document QA | use short retrieved chunks\nMath | use calculator/tool when possible\nTool calling | use strict schema and small examples\nGrammar help | classify the grammar task first, then answer\n\nFor example, instead of:\n\n\n    User gives a long messy paragraph and asks the model to understand everything, correct grammar, summarize, answer questions, and explain.\n\n\nyou can make a pipeline:\n\n\n    input\n      -> normalize\n      -> detect task type\n      -> remove irrelevant noise\n      -> split into smaller chunks\n      -> select the useful part\n      -> send short structured prompt to model\n\n\nFor a small model, this kind of pipeline is often more important than trying to make the model “smart enough” to handle arbitrary mess.\n\n* * *\n\n## 3. Train on realistic noise, not arbitrary garbage\n\nThere is a difference between useful robustness data and garbage data.\n\n### Good noisy SFT examples\n\nGood noisy examples teach the model to handle realistic user input:\n\n\n    typos\n    informal Persian\n    missing punctuation\n    mixed Persian-English terms\n    short unclear question\n    student misconception\n    slightly messy formatting\n\n\n### Bad noisy examples\n\nBad examples teach the model to imitate broken data:\n\n\n    citation fragments\n    broken references\n    HTML leftovers\n    random source names\n    duplicated lines\n    malformed dates\n    garbled bibliography text\n    OCR garbage\n    truncated sentences\n\n\nThe first kind can be useful for SFT.\n\nThe second kind should usually be removed from CPT data and from the “good” n-gram corpus.\n\nSo I would use this rule:\n\nNoise type | Use in training?\n---|---\nnatural human typo | maybe yes\ninformal Persian | yes, if target includes it\nstudent mistake | yes, for tutor SFT\nunclear user question | yes, if assistant learns to clarify\nWikipedia reference tail | no, remove or use as bad data\nbroken source list | no\nmalformed citation numbers | no\nduplicated boilerplate | no\nrandom mixed-language bibliography | no\n\n* * *\n\n## 4. About your Wikipedia example\n\nThe example you showed looks like this:\n\n  1. The beginning is normal Persian prose.\n  2. The ending becomes reference/citation garbage.\n  3. There are broken spaced numbers like `۲ ۸`, `۱ ۳ ۵ ۷`, `۱ ۳ ۳ ۲`.\n  4. Source names and article titles are glued into the sentence.\n  5. The passage boundary seems wrong.\n\n\n\nSo I would not treat that as a pure “bad document” problem.\n\nIt is more like:\n\n\n    good prose + bad trailing span\n\n\nThat means the best operation is often:\n\n\n    keep the good part\n    truncate the bad tail\n\n\nnot:\n\n\n    reject the whole passage\n\n\nand not:\n\n\n    let the n-gram model figure it out\n\n\nA good cleaning system should detect that the text changes from normal prose into citation/reference fragments.\n\n* * *\n\n## 5. Clean before training the Good n-gram model\n\nFor the Good n-gram model, only train on text that you would be happy for the model to imitate.\n\nIf the Good n-gram model sees citation tails, it may learn that citation tails are normal Persian.\n\nSo I would do this:\n\n\n    raw Wikipedia text\n      -> markup/HTML/boilerplate cleanup\n      -> paragraph split\n      -> line/sentence split\n      -> reference-tail removal\n      -> Persian normalization\n      -> deduplication\n      -> quality scoring\n      -> Good KenLM training corpus\n\n\nOnly after this should you train the Good n-gram model.\n\nThis is consistent with how large open corpora are usually built. For example:\n\n  * RedPajama discusses preprocessing Wikipedia to remove hyperlinks, comments, and formatting boilerplate.\n  * FineWeb emphasizes filtering and deduplication as central parts of dataset construction.\n  * FinerWeb-10BT shows that line-level filtering can improve data quality and training efficiency.\n\n\n\nThe practical lesson is:\n\n> Filtering is not something you add only at the end. It is part of corpus construction.\n\n* * *\n\n## 6. Use line/sentence-level cleaning, not only document-level cleaning\n\nYour example is exactly why document-level filtering is not enough.\n\nA document may contain:\n\n\n    good paragraph\n    good paragraph\n    good paragraph\n    bad reference tail\n\n\nIf you only classify the whole document as good/bad, you lose useful text.\n\nInstead, use smaller units:\n\nUnit | Use\n---|---\ndocument | broad quality / source metadata\nparagraph | main CPT unit\nline | boilerplate/reference detection\nsentence | fine-grained truncation\nspan | remove bad tail after good prose\n\nFor Wikipedia-like data, I would do:\n\n\n    article\n      -> sections\n      -> paragraphs\n      -> lines/sentences\n      -> score each unit\n      -> remove bad units\n      -> optionally merge clean neighboring units\n\n\nThis is especially useful for tails like:\n\n\n    ... normal Persian sentence. BBC Persian Abrahamian Modern Iran p۱ ۲ ۲ ...\n\n\nThe normal sentence can be kept. The reference tail should be removed.\n\n* * *\n\n## 7. Heuristics for reference-tail detection\n\nYou can start with simple heuristics.\n\nReject or truncate spans with:\n\n\n    many isolated numbers\n    too many digits\n    too many parentheses/brackets\n    URL / DOI / ISBN / ISSN patterns\n    English-heavy reference fragments\n    source names glued into Persian prose\n    bibliography-like patterns\n    repeated source names\n    very high punctuation ratio\n    very low Persian-letter ratio\n    abnormal spaced digits\n\n\nExamples of suspicious patterns:\n\n\n    ۲ ۸\n    ۱ ۳ ۵ ۷\n    ۱ ۳ ۳ ۲\n    p۱ ۲ ۲\n    ص ۲ ۸ ۳\n    BBC Persian\n    Modern Iran\n    ISBN\n    ISSN\n    doi\n    http\n    www\n\n\nFor Persian Wikipedia specifically, also watch for section/reference terms, but do not use them too naively.\n\nWords like:\n\n\n    منابع\n    ارجاع\n    پیوند\n    جستارهای وابسته\n    پانویس\n    کتابشناسی\n\n\ncan indicate reference sections, but context matters.\n\nFor example:\n\n\n    منابع طبیعی ایران\n\n\nis normal content, not a reference section.\n\nSo I would use these words mostly as:\n\n\n    section heading / line-level / end-of-article signal\n\n\nnot as a global document rejection rule.\n\n* * *\n\n## 8. Truncation is often better than rejection\n\nFor your example, I would probably do something like:\n\n\n    Before:\n    <good Persian prose>. <good Persian prose>. <citation/source garbage> <broken numbers> <bibliography tail>\n\n    After:\n    <good Persian prose>. <good Persian prose>.\n\n\nA practical rule:\n\n\n    If a paragraph starts as good Persian prose but later becomes citation-like,\n    truncate from the first suspicious boundary.\n\n\nPossible boundary signals:\n\n\n    sudden English source title\n    sudden bibliography author/title/page pattern\n    many spaced digits\n    multiple source names in a row\n    Persian sentence without punctuation followed by reference fragments\n\n\nThis is not perfect, but it is much better than letting the n-gram model learn the garbage.\n\n* * *\n\n## 9. Good LM / Bad LM setup\n\nYour n-gram idea can still be useful.\n\nI would use two n-gram models:\n\n### Good LM\n\nTrain on:\n\n\n    clean Persian prose\n    clean Wikipedia paragraphs\n    curated educational text\n    high-confidence manually accepted examples\n\n\n### Bad LM\n\nTrain on:\n\n\n    reference tails\n    citation fragments\n    boilerplate\n    broken OCR-like text\n    mixed-language bibliography\n    malformed Wikipedia tails\n    rejected OSCAR chunks\n\n\nThen score candidates with both.\n\nA candidate is better if:\n\n\n    Good LM likes it\n    Bad LM does not like it\n\n\nConceptually:\n\n\n    score = bad_lm_score - good_lm_score\n\n\nor any similar ratio/difference.\n\nDo not overthink the formula at first. The important idea is:\n\n> Good LM should model what you want. Bad LM should model what you want to remove.\n\nThis is better than a single perplexity threshold.\n\n* * *\n\n## 10. Persian normalization\n\nBefore scoring with n-gram models, normalize Persian consistently.\n\nUseful tools:\n\n  * Hazm\n  * Hazm GitHub\n  * PersianTools\n  * Lucene PersianNormalizer\n\n\n\nThings to normalize:\n\n\n    Arabic/Persian ي/ی\n    Arabic/Persian ك/ک\n    heh variants\n    Arabic/Persian digits\n    diacritics\n    extra tatweel/kashida\n    extra spaces\n    weird zero-width characters\n    punctuation spacing\n    half-space / ZWNJ\n\n\nFor example, Hazm’s normalizer is useful for standard Persian text normalization, including spacing and ZWNJ-related normalization.\n\n* * *\n\n## 11. Do not simply remove all ZWNJ\n\nFor Persian, ZWNJ is not just random noise.\n\nIt can be meaningful in words like:\n\n\n    کتاب‌ها\n    می‌روم\n    خانه‌ای\n    رفته‌ام\n\n\nSo I would not simply delete every zero-width non-joiner.\n\nBetter:\n\n\n    normalize/correct ZWNJ\n    remove weird repeated zero-width characters\n    standardize Unicode form\n    collapse multiple zero-width chars\n    keep valid Persian ZWNJ where appropriate\n\n\nPersian word segmentation and ZWNJ recognition are real NLP problems; see, for example, Joint Persian Word Segmentation Correction and ZWNJ Recognition.\n\nPractical rule:\n\n> Normalize ZWNJ; do not blindly remove it.\n\n* * *\n\n## 12. Digits: pick a convention\n\nPersian corpora often contain mixed:\n\n\n    Persian digits: ۱۲۳\n    Arabic digits: ١٢٣\n    Latin digits: 123\n    broken spaced digits: ۱ ۲ ۳\n\n\nYou should choose a convention.\n\nFor CPT prose, Persian digits may be natural.\n\nFor JSON, tool calling, math verification, and metadata, Latin digits are often easier.\n\nA practical approach:\n\nContext | Suggested convention\n---|---\nraw Persian prose CPT | Persian digits are okay\nmath internal answer field | Latin digits\nJSON/tool arguments | Latin digits\nfinal Persian display answer | Persian digits are okay\nmetadata | Latin digits\nbroken spaced digits | fix if obvious, otherwise reject/truncate\n\nThe important thing is consistency.\n\nDo not let this happen randomly:\n\n\n    ۲ 8 ٣ ۴ 5\n\n\nunless you intentionally want mixed-digit robustness data.\n\nFor your example, `۲ ۸ مرداد` should probably become:\n\n\n    ۲۸ مرداد\n\n\nand `۱ ۳ ۵ ۷` should become:\n\n\n    ۱۳۵۷\n\n\nif you are confident it is a date/year.\n\nBut if the number sequence is ambiguous, reject or truncate that span.\n\n* * *\n\n## 13. A simple cleaning pipeline for your current case\n\nI would implement something like this:\n\n\n    1. Extract text\n    2. Normalize Unicode\n    3. Normalize Persian letters\n    4. Normalize digits\n    5. Split into paragraphs\n    6. Split paragraphs into sentences/lines\n    7. Detect reference-like lines/spans\n    8. Truncate bad tails\n    9. Remove very bad paragraphs\n    10. Deduplicate\n    11. Train Good LM on clean accepted text\n    12. Train Bad LM on rejected tails/noise\n    13. Score new chunks\n    14. Manually audit samples\n\n\nMore concrete:\n\n\n    input paragraph\n      -> sentence split\n      -> for each sentence/span:\n           calculate Persian letter ratio\n           calculate digit ratio\n           calculate Latin ratio\n           calculate punctuation ratio\n           detect citation markers\n           detect spaced-digit patterns\n           detect source-name/reference tail\n      -> if bad tail starts after good text:\n           keep text before bad tail\n      -> else if whole paragraph is bad:\n           reject\n      -> else:\n           keep\n\n\n* * *\n\n## 14. Example pseudo-code\n\nVery rough pseudo-code:\n\n\n    import re\n\n    PERSIAN_LETTERS = r\"آ-ی\"\n\n    def persian_ratio(text):\n        letters = re.findall(f\"[{PERSIAN_LETTERS}]\", text)\n        chars = [c for c in text if not c.isspace()]\n        return len(letters) / max(1, len(chars))\n\n    def digit_ratio(text):\n        digits = re.findall(r\"[0-9۰-۹٠-٩]\", text)\n        chars = [c for c in text if not c.isspace()]\n        return len(digits) / max(1, len(chars))\n\n    def has_spaced_digits(text):\n        # examples like \"۱ ۳ ۵ ۷\" or \"۲ ۸\"\n        return bool(re.search(r\"[0-9۰-۹٠-٩](\\s+[0-9۰-۹٠-٩]){1,}\", text))\n\n    def looks_reference_like(text):\n        patterns = [\n            r\"http\",\n            r\"www\\.\",\n            r\"doi\",\n            r\"ISBN\",\n            r\"ISSN\",\n            r\"BBC Persian\",\n            r\"Modern Iran\",\n            r\"\\bp\\s*[0-9۰-۹٠-٩]\",\n            r\"ص\\s*[0-9۰-۹٠-٩]\",\n        ]\n        return any(re.search(p, text, flags=re.IGNORECASE) for p in patterns)\n\n    def is_bad_tail(sentence):\n        if looks_reference_like(sentence):\n            return True\n        if has_spaced_digits(sentence) and digit_ratio(sentence) > 0.08:\n            return True\n        if persian_ratio(sentence) < 0.45:\n            return True\n        return False\n\n    def truncate_bad_tail(sentences):\n        clean = []\n        for sent in sentences:\n            if is_bad_tail(sent) and len(clean) > 0:\n                break\n            if not is_bad_tail(sent):\n                clean.append(sent)\n        return \" \".join(clean)\n\n\nThis is only a starting point. You would need to tune it by looking at accepted/rejected samples.\n\n* * *\n\n## 15. Manual audit is still necessary\n\nDo not trust the cleaner blindly.\n\nAfter each cleaning rule change, sample:\n\n\n    100 accepted chunks\n    100 rejected chunks\n    100 truncated chunks\n\n\nThen inspect:\n\nSample type | What to check\n---|---\naccepted | Did garbage survive?\nrejected | Did good Persian get removed?\ntruncated | Did truncation cut at the right place?\nborderline | Should this become a new rule?\n\nThis is the same kind of loop you are already doing:\n\n\n    clean -> review -> adjust rules -> clean again\n\n\nThat loop is the correct approach.\n\n* * *\n\n## 16. For /7: how to train the small model for noisy prompts\n\nFor the 0.8B model, I would not train it on arbitrary noisy prompts.\n\nI would train it on controlled noisy prompts.\n\nExamples:\n\n### Good robustness SFT\n\n\n    User has typo -> assistant still answers.\n    User asks unclear question -> assistant asks clarification.\n    User includes irrelevant sentence -> assistant focuses on main question.\n    User asks two things -> assistant separates them.\n    User provides messy Persian -> assistant normalizes meaning.\n\n\n### Bad robustness SFT\n\n\n    User prompt contains random citation garbage -> assistant imitates it.\n    User prompt has broken reference tail -> assistant treats it as meaningful.\n    User prompt has unrelated source list -> assistant summarizes garbage.\n\n\nFor a small model, the best behavior is often:\n\n\n    I cannot reliably answer from this messy text. Please provide a clearer sentence.\n\n\nor:\n\n\n    The first part is understandable, but the ending looks like broken reference text.\n\n\nThat is a valid assistant behavior.\n\nThe goal is not to make the 0.8B model magically robust to every bad prompt. The goal is to make it fail gracefully.\n\n* * *\n\n## 17. Product-level strategy for a 0.8B assistant\n\nFor a low-end-device assistant, I would design guardrails around the model.\n\n\n    raw input\n      -> normalization\n      -> task classifier\n      -> noise detector\n      -> chunk selector\n      -> small model\n      -> output validator\n\n\nIf the input is too messy:\n\n\n    ask clarification\n\n\nIf the input is too long:\n\n\n    split/summarize first\n\n\nIf it contains reference garbage:\n\n\n    remove or warn\n\n\nIf it asks for math:\n\n\n    use calculator/tool if available\n\n\nIf it asks for grammar:\n\n\n    route to grammar-tutor prompt template\n\n\nSmall models work better when the surrounding system reduces ambiguity.\n\n* * *\n\n## 18. Bottom line\n\nFor the noisy prompt question:\n\n> Larger models generally handle long, noisy, multi-constraint prompts better. But a 0.8B model can still be useful if you reduce the prompt burden with preprocessing, task routing, shorter context, templates, and controlled robustness SFT.\n\nFor the n-gram cleaning question:\n\n> Do not let the n-gram model handle structural garbage by itself. Remove obvious reference/citation/wiki-tail noise first. Train the Good LM only on text you want the model to imitate. Optionally train a Bad LM on the garbage you want to detect.\n\nThe most important rule is:\n\n> The n-gram model should be a quality scorer, not a garbage collector.",
  "title": "How can i build a High Quality dataset?"
}