sabin1234/Future_Education_Nepal_MBBS_FAQ_Dataset

Future Education Nepal — MBBS FAQ Dataset (Nepali)

Overview

This dataset (future_education_nepal_mbbs_faq_nepali_sharegpt.jsonl) is a collection of 38 question-answering conversation pairs in Nepali, covering frequently asked questions about studying MBBS (medicine) in Nepal — primarily aimed at Indian students considering Nepal as a study destination. Each record is a single-turn human↔gpt exchange in ShareGPT-style format: a Nepali-language question about MBBS admissions, fees, recognition, or student life, followed by a short, factual Nepali-language answer.

The source is labeled "Future Education - MBBS in Nepal", and unlike the two other datasets in this series, this one is marked generation_type: human-curated and condition: real-world (rather than real) — suggesting the Q&A content was curated by humans from real informational material about MBBS study in Nepal, then formatted into an instruction-tuning conversation structure.

• —File format: JSON Lines (.jsonl), one JSON object per line
• —Total records: 38
• —Source: Future Education - MBBS in Nepal (repo: Future Education)
• —License: Apache-2.0 (permissive tier)
• —Language: Nepali (ne / ISO 639-3 npi), Devanagari script (Deva)
• —Task type: question-answering
• —Generation type: human-curated
• —Condition: real-world
• —Source revision: v1

File / Record Structure

Every line is a JSON object with the following top-level fields:

metadata_json sub-fields (per record)

This dataset's metadata schema is leaner than the HIB health insurance FAQ dataset — it has no question_type, question_length, response_length, maximum_response_sentences, minimum_reasoning_dimensions, or minimum_complexity_score fields, and no behavior_definition field either (only the short behavior label).

Distribution Analysis

Behavior Distribution

Only one behavior type appears across the entire dataset:

Domain / Category / Sub-domain Distribution

This is a single-domain, single-category, single-sub-domain dataset — every field-level metadata value is constant. All topical variety lives in the question text itself. Reading through the 38 questions, they cluster into these practical themes (a single question sometimes touches more than one):

1. 1.Recognition & degree validity (~7 questions): whether an MBBS from Nepal is valid in India, USA/UK/Australia, WHO recognition of Nepal, comparing Nepal's MBBS to India's private MBBS, comparing Nepal vs. Russia for MBBS
2. 2.Admissions & eligibility (~9 questions): NEET requirement and minimum score, entrance exam process, required documents, minimum age, admission process for 2026, what happens if a student fails an exam, whether local-language learning is required
3. 3.Fees & duration (~2 questions): total MBBS fee in Nepal, program duration
4. 4.Universities & institutions (~3 questions): number of NMC-approved universities, list of major NMC-approved universities, which city is best for MBBS study
5. 5.Student life & logistics for Indian students (~9 questions): cost of living, student life experience, safety, food situation, hostel facilities, weather/climate adaptation, whether parents can visit, visa-free entry for Indian citizens
6. 6.Post-graduation & career (~6 questions): FMGE pass percentage for Nepal graduates, best FMGE prep strategy, pursuing MD/MS in India afterward, career options after MBBS, internship opportunities
7. 7.Key terms/exams explained (~4 questions): what is NMC, what is FMGE and its difficulty, what is NEXT and whether it replaces FMGE, medium of instruction (English)
8. 8.Scale/statistics (~1 question): current number of Indian MBBS students in Nepal

Content Language / Script Distribution

Purely monolingual Nepali content in Devanagari script — no English or code-switching permitted, even though the subject matter (Indian students studying MBBS abroad) is inherently cross-border.

Task Type / Generation Type / Condition Distribution

Notably, this differs from the other two datasets in the series (SAC Nepal health/fitness FAQ and HIB health insurance FAQ), which both use task_type: instruction-following and generation_type/condition: real. This dataset instead uses task_type: question-answering and generation_type: human-curated / condition: real-world — indicating a slightly different data-generation pipeline or labeling convention was used for this source.

Other Constant Fields

The following fields are identical across all 38 records (no variation to report): source, source_name, source_repo, source_config, source_split, source_revision, language, language_code, script, license, license_tier, and url (always empty).

Conversation Structure

Single-turn instruction/response format — no multi-turn dialogue.

ID Structure

• —id: a sg_ prefix followed by a 32-character hex hash (likely an MD5-style hash), all 38 unique
• —source_row_id: identical hash to id, with a :1 suffix appended (e.g. sg_09138666ec5037d5cc53e4dcff3baea7:1) — the :1 likely denotes "turn/segment 1" from the original ShareGPT-format source record

Text Length Statistics

Questions are short, single-sentence prompts. Answers are moderately detailed — averaging ~261 characters, roughly in between the SAC Nepal fitness dataset (~121 chars) and the HIB health insurance dataset (~327 chars) — consistent with the behavior label calling for "short and informative" answers.

Summary

This dataset is a narrow, single-purpose Q&A corpus: 38 short Nepali-language question/answer pairs, all about MBBS study in Nepal from the perspective of prospective (largely Indian) medical students. Every record shares the same domain (Education → MBBS in Nepal), the same sub-domain (MBBS admission and study), and the same behavior instruction ("short, informative answer"). There is no variation across domain, category, license, task type, or content-language settings — the only real variety is in the specific sub-topic of each question, spanning degree recognition, admissions/eligibility, fees, universities, student life/logistics, post-graduation career paths, key exam/regulatory terms (NMC, FMGE, NEXT), and enrollment statistics.

Compared to its sibling datasets:

• —SAC Nepal (health/fitness): broader health domain, instruction-following/real labeling, shortest answers (~121 chars avg)
• —HIB (health insurance): narrower "Health Insurance" domain, richest metadata (question/response length + complexity constraints), longest answers (~327 chars avg)
• —Future Education (MBBS): Education domain, question-answering/human-curated/real-world labeling, leanest metadata schema, mid-length answers (~261 chars avg)

This kind of dataset is well suited for fine-tuning or evaluating a language model's ability to answer study-abroad/medical-education FAQs accurately and concisely in Nepali, or as a domain-specific supplement (education vertical) alongside the health-focused datasets already reviewed in this series.