sabin1234/Pharmacy_Licensing_Exam_FAQ

💊 Pharmacy Licensing Exam FAQ (Nepali) — Dataset README

A 100% Nepali-language synthetic reasoning dataset built from a single row-pair of official Nepal Department of Health Services exam-result data — turned into 100 question-answer pairs covering counts, percentages, ratios, statistics, and "what-if" arithmetic.

🔖 TL;DR (At a Glance)

🗂️ Schema — What's Inside Each Record

{
  "id": "sg_b39dea595f5f3f189ce06472bec1577e",
  "conversations": [
    {"from": "human", "value": "How many pharmacists attended the exam?"},
    {"from": "gpt",   "value": "110 pharmacists attended the exam."}
  ],
  "source": "Department of Health Services",
  "source_name": "pharmacy_licensing_exam_faq",
  "source_repo": "Department of Health Services",
  "source_config": "default",
  "source_split": "train",
  "source_revision": "v1",
  "source_row_id": "Licensing_Examination_Results_Pharmacists_Pharmacy_FY2071_72_to_2073_74_merged.csv:row_0,row_1:1",
  "language": "ne", "language_code": "npi", "script": "Deva",
  "license": "Creative Commons Attribution", "license_tier": "permissive",
  "task_type": "instruction-following",
  "generation_type": "synthetic", "condition": "synthetic",
  "url": "",
  "metadata_json": "{...}"
}

Field-by-field status

Inside metadata_json (nested) — also all constant

{
  "generation_domain": "Public Health Exam Statistics",
  "generation_category": "Pharmacy Licensing Exam Results",
  "generation_sub_domain": "Direct Q&A and basic statistical reasoning",
  "behavior": "Giving direct and natural answers",
  "behavior_definition": "Answering directly, naturally, and concisely in pure Nepali, using only the given values.",
  "question_type": "Direct conversational question",
  "content_language": "Nepali",
  "content_script": "Devanagari",
  "english_content_allowed": false
}

The generation_sub_domain — "basic statistical reasoning" — is the key differentiator from the NEPSE dividend dataset: this collection isn't pure fact-lookup, it's arithmetic/statistical reasoning over two numbers.

⚠️ The Most Important Finding: All 100 Records Come From Just 2 Source Rows

Unlike a typical FAQ dataset covering many companies/years, every single one of the 100 records is derived from exactly one row-pair in one CSV file:

Licensing_Examination_Results_Pharmacists_Pharmacy_FY2071_72_to_2073_74_merged.csv
  → row_0 (Pharmacist results)
  → row_1 (Pharmacy-candidate results)

Despite the filename suggesting the CSV merges FY 2071/072 through FY 2073/074, only the fiscal-year-2071/072 row pair was actually sampled — confirmed by record #9, whose answer states both groups belong to FY 2071/072. So this is not a broad exam-history dataset; it's a single exam sitting, exhaustively interrogated 100 different ways.

The underlying two data points

Everything else in the dataset — sums, differences, ratios, percentages, means, medians, hypotheticals — is arithmetic derived from just these figures.

💬 Conversation Structure

• —Every record has exactly 2 turns: human → gpt. 100/100 consistent, no system prompt.
• —Questions and answers are noticeably shorter and more varied in shape than a fixed-template dataset:

• —Unique questions: 100 / 100 (no exact duplicate questions)
• —Unique answers: 93 / 100 — 7 answers repeat across unrelated questions (see Data Quality below)

❓ What Kinds of Questions Does It Ask?

Unlike the NEPSE dividend dataset (6 fixed templates), this dataset has no small fixed template set — it's built around a wide range of statistical/arithmetic reasoning operations applied to the same two numbers. Keyword frequency across the 100 questions:

Beyond these, the dataset includes several other question families:

• —Direct counts — "How many pharmacists attended/passed/failed?"
• —Comparisons — "Which group had more/fewer attendees or passes?"
• —Combined totals — "How many candidates attended/passed across both groups combined?"
• —Pass-to-attendance ratios — expressed both as fractions and as X:Y ratios
• —Percentage-point gaps — "How many percentage points higher is the pharmacy pass rate?"
• —Hypothetical / what-if scenarios — "If 5 more pharmacists had passed, what would the new pass rate be?", "If both groups improved to a 90% pass rate, how many total passes would that require?"
• —Statistics — mean, median, and range across the two groups' figures
• —Products — "What is the product of the two attendance figures?" (110 × 481 = 52,910)

This is a reasoning-over-two-numbers dataset, not a fact-recall dataset — the opposite design philosophy from the NEPSE dividend FAQ collection.

🔁 Duplicates & Data Quality

⚠️ Mismatched/copy-pasted answers (7 duplicate answers, 15 affected questions)

The most notable quality issue: several distinct questions share an identical answer that doesn't actually match what was asked. For example:

Two smaller cases follow the same pattern — an answer about "column value" or "fewer attendees" gets reused for a differently-worded question about passes. This looks like a generation-pipeline bug where the answer for one templated question in a sequence occasionally leaked into a neighboring question — 7 of the 100 answers (affecting 15 questions total) are affected.

⚠️ Garbled/corrupted tokens despite english_content_allowed: false

8 records contain stray, non-Nepali fragments embedded mid-sentence — most likely corrupted renderings of English connector words like "more" or "or":

"४१ mवाe फार्मेसी उम्मेदवार पास भएनन्।"
"फार्मासिस्ट ओगटेका fवा लगभग २०.२९% मध्ये नपास उम्मेदवार।"
"२७५ mवाe फार्मेसी उपस्थित।"

These fragments (mवाe, fवा) break the dataset's stated Devanagari-only guarantee and read as broken/untranslated artifacts rather than intentional content — worth cleaning or filtering before downstream use.

Other notes

• —92 / 100 answers contain at least one number (Devanagari or Arabic numerals) — a handful of qualitative/comparison answers have none.
• —No missing fields, no turn-structure violations, clean UTF-8 elsewhere.

🎨 Design Philosophy (the likely thinking behind this dataset)

1. 1.Depth over breadth — instead of covering many exams across many years (like the NEPSE dataset covers many companies), this dataset takes one exam result and asks it 100 different ways, testing whether a model can perform varied arithmetic/statistical reasoning over the same two numbers.
2. 2.Reasoning-focused, not recall-focused — heavy use of ratios, percentages, means, medians, ranges, and hypothetical "what if N more had passed" scenarios, rather than simple fact lookup.
3. 3.Real-world grounding, synthetic construction — the underlying numbers come from a real Department of Health Services exam-results file, but the Q&A pairs themselves are machine-generated (generation_type: synthetic), which likely explains the answer-mismatch and garbled-token issues found above.
4. 4.Nepali-only intent, imperfectly executed — english_content_allowed: false is declared, but a small number of records leak corrupted non-Nepali fragments, suggesting a translation/generation step didn't fully complete.

🧪 Sample Records

{"id": "sg_b39dea595f5f3f189ce06472bec1577e",
 "conversations": [
   {"from": "human", "value": "How many pharmacists attended the exam?"},
   {"from": "gpt", "value": "110 pharmacists attended the exam."}
 ]}

{"id": "sg_...", 
 "conversations": [
   {"from": "human", "value": "What is the ratio of pharmacy to pharmacist attendance?"},
   {"from": "gpt", "value": "The attendance ratio is 481:110, or approximately 4.37:1."}
 ]}

⚠️ Things to Watch Out For When Using This Dataset

• —Extremely narrow source coverage — all 100 Q&A pairs derive from a single exam sitting (2 rows). This is not representative of pharmacy licensing results across years or exam types; treat it as a reasoning-skill dataset, not a knowledge-coverage dataset.
• —~7% of answers are mismatched to their question due to an apparent generation bug — worth filtering or manually correcting before training/production use.
• —8% of records contain corrupted non-Nepali fragments (mवाe, fवा) — worth a cleanup pass.
• —Synthetic, not real — unlike a real-world-grounded FAQ set, some hypothetical/statistical answers (e.g., "if both groups improved to 90%...") involve derived/approximated figures rather than directly-reported facts; verify precision-sensitive values before relying on them.

📜 License

Creative Commons Attribution (permissive) — free to use with attribution.

This README was compiled from a full programmatic analysis of all 100 records in the dataset (all figures calculated directly from the file, not estimated).