sabin1234/NEPSE_Dividend_FAQ

📈 NEPSE Dividend FAQ (Nepali) — Dataset README

"How much dividend did it give this time?" — the question every stock market watcher asks, now turned into 2,000 question-answer pairs.

A simple, clean, 100% Nepali-language financial FAQ dataset that builds question-answer (Q&A) pairs about the total dividend and bonus share dividend of companies listed on NEPSE. This file is a full dissection of that exact dataset — every field, every pattern, everything.

🔖 TL;DR (At a Glance)

🗂️ Schema — What's Inside Each Record

Each line is an independent JSON object, and all of them share the same structure:

{
  "id": "nepse_dividend_0001",
  "conversations": [
    {"from": "human", "value": "What was ACLBSL's total dividend for fiscal year 2077/078?"},
    {"from": "gpt",   "value": "ACLBSL's total dividend for fiscal year 2077/078 was 21%."}
  ],
  "source": "NEPSE Dividend History",
  "language": "ne", "language_code": "npi", "script": "Deva",
  "license": "Apache-2.0", "license_tier": "permissive",
  "task_type": "instruction-following",
  "generation_type": "real", "condition": "real",
  "metadata_json": "{...}"
}

Field-by-field status

Interesting fact: almost all dataset-metadata fields (source, license, language, tasktype, generationtype...) are 100% constant — meaning this is a homogeneous dataset built from a single "batch" using a single recipe. All the variation lives in the actual Q&A content — the company, the fiscal year, and the dividend percentage.

Inside metadata_json (nested) — also all constant!

{
  "generation_domain": "Financial Services",
  "generation_category": "NEPSE Dividend History",
  "generation_sub_domain": "Company Dividends",
  "behavior": "Providing factual dividend information",
  "behavior_definition": "Directly answering the question using dividend data for the specified company and fiscal year.",
  "question_type": "Fact-based question",
  "content_language": "Nepali",
  "content_script": "Devanagari",
  "english_content_allowed": false
}

All 9 of these sub-fields are identical across 2,000 out of 2,000 lines — the behavior is a single, focused capability: "providing fact-based company dividend information." There's no multi-behavior mixing, and no opinion/analysis-style questions — pure facts only.

💬 Conversation Structure

• —Each record has exactly 2 turns: human (question) → gpt (answer). Never 3-turn, 1-turn, or follow-up — this pattern holds for 100% of 2,000 records.
• —There is no system prompt.
• —Length is also very tight/consistent:

Short, direct, single-fact Q&A — this is not a long conversation or reasoning-chain dataset. It's a quick-recall FAQ style.

❓ Question Pattern Distribution — Exactly 6 Templates

Once you strip out the company code and fiscal year, all 2,000 questions fit into one of these 6 templates (100% coverage, no outliers):

Template 1 ██████████████████████ 23.4%
Template 2 █████████████████████ 22.4%
Template 3 ████████████████████ 20.1%
Template 4 ███████████ 11.6%
Template 5 ███████████ 11.6%
Template 6 ███████████ 11.1%

Templates 1–3 = 3 phrasings asking about Total Dividend (1,317 records — 65.9%) Templates 4–6 = 3 phrasings asking about Bonus Share dividend (683 records — 34.1%)

For each "content" type (total/bonus), exactly 3 syntactic variations are provided — to give sentence-level diversity. But semantically, they're all just two kinds of questions.

✅ Answer Pattern Distribution — Exactly 2 Templates

The answers are even cleaner — there are only 2 templates total:

No hedging, no extra context, no "this is an estimate"-style disclaimers — a straight fact → answer, in a deterministic format. This design reveals the dataset's purpose: "given a specific company + specific year, the model should give a clean, single-line, fact-based answer."

🏢 Company Coverage

• —211 distinct company codes are covered (e.g., EBL, NABIL, ADBL, GBIME...).
• —Each company has an average of ~9.5 questions, but the distribution is quite skewed — some companies have data spanning many years, others have only one or two.

Top 15 Companies by Question Count

Records-per-Company Density (histogram)

Long-tail distribution — 20 companies have only 1 question each, while top-tier companies (large banks/finance companies like EBL, NABIL, GBIME) have up to 30 years of history covered. This reflects real-world data availability — older/larger companies have more historical records available, while newer/smaller ones have less.

📅 Fiscal Year Coverage

Fiscal years from 2062/063 to 2082/083 (nearly a 20-year range) are covered — but density is higher toward recent years:

2067–069  ████████████████
2070–072  ███████████████████████
2073–075  ███████████████████████████████
2076–078  ██████████████████████████████████████
2079–081  ████████████████████████████████████████
2082–083  ███████

FY 2081/082 has the most records (206), while the earliest years (062-067) have very few (just 3 total) — a natural recency bias, since older data is less digitized/available.

💰 Dividend % Distribution

• —Median: 13.5% | Average: ~19.7% (pulled upward by outliers)
• —Most frequent single values: 10% (124 times), 15% (120 times), 0% (114 times), 20% (102 times)
• —392 distinct % values appear in total — many of them include decimals (e.g., 10.53%, 21.05%, 15.79%), which look like exact figures from actual bonus-share conversions, not rounded numbers.

🥇 The Highest Dividend (an interesting outlier!)

UNL appears to have declared a total dividend of 990% in FY 2071/072 and 860% in FY 2070/071.

This could be a data-entry error (e.g., 99.0% mistakenly entered as 990%), or it could genuinely reflect a bonus-heavy year for a micro-finance-type company back then. If you plan to use this dataset for training/production, it's a good idea to audit such extreme outliers.

🎁 Bonus Share vs. Total Dividend

• —Only 1 total-dividend record shows 0% — almost every company/year has some form of dividend.
• —But 113 bonus-share records show 0% — meaning many companies didn't distribute bonus shares in certain years (cash dividend only), which matches real NEPSE practice.

🔁 Duplicates & Data Quality

Overall, this dataset is very clean — almost production-ready for building FAQ pairs. Aside from the ~990% outlier mentioned above, no major errors were found.

🎨 Design Philosophy (the likely thinking behind this dataset)

1. 1.Single, focused behavior — it does one thing: given a company + year, return the % dividend. No multi-task mixing.
2. 2.Controlled syntactic diversity, fixed semantics — 6 question templates but the same underlying meaning (3 total + 3 bonus) — this seems aimed at making the model phrasing-robust (able to answer correctly no matter how the same information is asked).
3. 3.Real-world grounding — generation_type: real — not synthetic/fictional data, but drawn from actual NEPSE dividend history (hence the outliers/uneven distribution — the real world is messy!).
4. 4.Deterministic, hedge-free answers — no words like "approximately" or "estimated." This establishes raw ground truth for factual-recall tasks.
5. 5.Nepali-only, Devanagari-only — english_content_allowed: false — no Roman/English mixed in anywhere, full linguistic purity in Nepali is maintained.

🧪 Sample Records

{"id": "nepse_dividend_0001",
 "conversations": [
   {"from": "human", "value": "What was ACLBSL's total dividend for fiscal year 2077/078?"},
   {"from": "gpt", "value": "ACLBSL's total dividend for fiscal year 2077/078 was 21%."}
 ]}

{"id": "nepse_dividend_0231",
 "conversations": [
   {"from": "human", "value": "What is NICL's bonus share dividend percentage for fiscal year 2074/075?"},
   {"from": "gpt", "value": "NICL's bonus share dividend for fiscal year 2074/075 was 10.53%."}
 ]}

⚠️ Things to Watch Out For When Using This Dataset

• —Coverage is uneven — some companies/years have a lot of data, others just one record. Watch out for class imbalance when fine-tuning.
• —Only company codes are given, not full names (e.g., "EBL" — presumably Everest Bank Limited) — if full names are needed, a separate mapping table is required.
• —Outliers like the 990% figure should be audited.
• —2-turn, single-fact QA only — this dataset doesn't teach multi-turn conversation, reasoning, or comparative analysis ("which company paid more?"). If that kind of capability is needed, it should be combined with another dataset.

📜 License

Apache-2.0 (permissive) — free for both commercial and non-commercial use, with attribution.

This README was auto-generated — based on a programmatic analysis of the full 2,000 records (all figures calculated from the actual dataset, not estimated).