Somtharu181coder/SFT_Dataset_domain_social

Nepali Social Studies MCQ — SFT Dataset

A cleaned, deduplicated, bias-corrected instruction-tuning dataset of Nepali-language multiple-choice questions on social studies topics, derived from the Aya Dataset.

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Dataset Summary

Rows	27,891
Language	Nepali (ne / npi), Devanagari script
Task type	Instruction-following (single-turn MCQ Q&A)
Domain	Social studies (सामाजिक) — MCQ only
License	Apache-2.0 (permissive)
Source	CohereLabs/aya_dataset (config: default, split: train), revision f9ea04583f02a8f86404ff6c58bf75fe637df8a2
Source subset	aya_human_nepali
Generation type	Synthetic (originally human-authored source, machine-processed pipeline)
Format	JSONL, one JSON object per line

This is a processed derivative of a single upstream slice (aya_human_nepali). It is not a general-purpose Nepali instruction dataset — every row is a 4-option social studies multiple-choice question.

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File Structure

Each line is a JSON object:

{
  "id": "sg_25728771e8644154729b5458133abea1",
  "conversations": [
    {"from": "human", "value": "समाजमा शान्ति कायम गर्न के आवश्यक छ? क) द्वन्द्व ख) घृणा ग) ईर्ष्या घ) सद्भाव"},
    {"from": "gpt", "value": "घ) सद्भाव"}
  ],
  "source": "CohereLabs/aya_dataset:default:train",
  "source_name": "aya_human_nepali",
  "source_repo": "CohereLabs/aya_dataset",
  "source_config": "default",
  "source_split": "train",
  "source_revision": "f9ea04583f02a8f86404ff6c58bf75fe637df8a2",
  "source_row_id": "sg_25728771e8644154729b5458133abea1:1",
  "language": "ne",
  "language_code": "npi",
  "script": "Deva",
  "license": "Apache-2.0",
  "license_tier": "permissive",
  "task_type": "instruction-following",
  "generation_type": "synthetic",
  "condition": "synthetic",
  "url": "",
  "metadata_json": "{\"generation_domain\": \"सामाजिक\", \"generation_category\": \"सामाजिक\", \"question_type\": \"बहुविकल्पीय\", \"question_length\": \"अति छोटो तथा छोटो\", \"content_language\": \"नेपाली\", \"content_script\": \"देवनागरी\"}"
}

Field reference

Field	Type	Notes
id	string	Unique per row. No duplicates.
conversations	array[2]	Exactly one human turn (question + 4 options) and one gpt turn (labeled answer).
source*	string	Full upstream provenance chain, constant across the dataset (single source).
language / language_code / script	string	ne / npi / Deva for every row.
license / license_tier	string	Apache-2.0 / permissive for every row.
task_type	string	instruction-following for every row.
generation_type / condition	string	synthetic for every row.
url	string	Always empty — no upstream URL was recorded for this source.
metadata_json	string (JSON-encoded)	See below. Must be json.loads'd — it's stored as a string, not a nested object.

metadata_json sub-fields (all constant across the dataset)

Sub-field	Value	Meaning
generation_domain	सामाजिक	Social studies
generation_category	सामाजिक	Social studies
question_type	बहुविकल्पीय	Multiple choice
question_length	अति छोटो तथा छोटो	Very short / short
content_language	नेपाली	Nepali
content_script	देवनागरी	Devanagari

MCQ format convention

• Options are always labeled क) ख) ग) घ) (Devanagari equivalents of A/B/C/D), embedded in the human turn after the question stem.
• The gpt turn is always <label>) <option text> — the label plus the exact option text, space-separated.

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Statistics

Metric	Value
Total rows	27,891
Question length (chars)	min 51 · median 105 · mean 105.5 · max 185
Answer length (chars)	min 5 · median 18 · mean 18.2 · max 53
Unique question strings	27,889 (2 collisions — see Known Issues)
Answer-option label distribution	क) 21.9% · ख) 27.8% · ग) 25.4% · घ) 24.8%

The answer-label distribution is intentionally near-uniform (see Processing History).

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Processing History

This file is the output of a 3-stage cleaning pipeline applied to a raw aya_human_nepali export (originally 29,029 rows):

Stage 1 — Structural validation Verified JSON validity, schema consistency, non-empty turns, and unique IDs. No rows dropped at this stage.

Stage 2 — Foreign-script / homoglyph contamination removal The raw export had characters from ~18 unrelated Unicode scripts (Armenian, Gujarati, Gurmukhi, Greek, Arabic, Bengali, Cyrillic, Telugu, Hangul, Kannada, Malayalam, Hebrew, Sinhala, Georgian, Thai, Ethiopic, Oriya) substituted into what should have been pure Devanagari text — e.g. रहित corrupted to रहಿತ. 1,117 rows were auto-removed for this reason (a smaller number of visually similar corruptions remain — see Known Issues).

Stage 3 — MCQ answer-position rebalancing The raw export had 84.8% of correct answers sitting in option क) (position 1) — a positional bias a model would learn to exploit instead of reading the question. Each valid, uncorrupted MCQ had its 4 options deterministically shuffled (seeded by row id, reproducible) and the question/answer text rewritten to match. 27,891 rows were successfully rebalanced; 21 rows where the correct option couldn't be confidently identified (corrupted text) were excluded rather than guessed.

29,029 → −1,117 (contamination) → −21 (unresolvable MCQ) → 27,891 final rows

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Known Issues (as of this file)

These are documented, not hidden — check before using for training or eval.

1. Residual character-level corruption (~135 rows / 0.5%) The contamination filter used in Stage 2 didn't cover every Unicode block. Confirmed residual cases:

   • Myanmar vowel signs (e.g. भूमिकသ — U+1031)
   • Arabic Presentation Forms-B (e.g. a stray U+FEEC inside a Nepali word)
   • Latin Extended-A ligatures/diacritics (ğ, œ) inside Nepali words
   • One Private Use Area character (U+F8FF)
   • Stray combining diacritics with no base character (U+0308) These rows are not flagged in this file and should be filtered before training if exact cleanliness matters.

2. 2 duplicate question pairs introduced by rebalancing Two pairs of rows shared the same question stem and same 4-option pool in the source data (just in different original order/answer) — a form of near-duplication the literal-string dedup step didn't catch pre-rebalance. Independent shuffling coincidentally produced identical final text for each pair:

   • sg_870ce75e2773760fbd9b4cb2b69cd555 / sg_ebe54de3e99d4de5f124c9ca0dd83d14
   • sg_e7783f31aec816aef8f9d6786a267c0c / sg_8c4838103eb655741dc0fb744d2bf5b5

3. Zero diversity by design, not by accident 100% of rows are: single source (aya_human_nepali), single domain (सामाजिक), single question type (MCQ), single length bucket (short). This is a narrow, homogeneous slice. If broader Nepali SFT coverage is the goal, this file needs to be combined with other domains/sources — it is not a general-purpose instruction dataset on its own.

4. No held-out split All 27,891 rows are from source_split: train. There is no dev/test split in this file — carve one out before using for evaluation.

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Recommended Use

• Suitable as one ingredient in a larger Nepali instruction-tuning mix, specifically for MCQ-style social studies knowledge.
• Not suitable on its own for general instruction-following, open-ended generation, or any domain outside social studies MCQs — the model will overfit to this narrow format if trained on it in isolation.
• Filter or manually review the ~135 residual-corruption rows and the 2 duplicate pairs above before final training use.

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License

Apache-2.0, inherited from the upstream CohereLabs/aya_dataset. Verify this still applies to your specific use case and jurisdiction — Apache-2.0 covers the dataset structure/text; check upstream terms for any additional conditions CohereLabs may have attached to the Aya Dataset specifically.

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Citation / Provenance

If publishing or citing this dataset, credit the upstream source:

Source: CohereLabs/aya_dataset (config: default, split: train)
Revision: f9ea04583f02a8f86404ff6c58bf75fe637df8a2
Subset: aya_human_nepali

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Changelog

Version	Rows	Change
Raw export	29,029	Original aya_human_nepali pull
v1 cleaned	29,029	Structural validation only (no removals)
v2 cleaned	27,891	+ foreign-script contamination removal (−1,117) + MCQ rebalancing (−21 unresolvable)