Hugging Face HealthInsuranceDataset (vraman54) Data

A 48-row extractive question-answering table in classic SQuAD shape, uploaded to the Hugging Face Hub under the vraman54 account. Every row carries five things: a background Context passage describing a health insurance policy provision, a natural-language Question about it, the Answer span lifted verbatim out of that passage, an integer Answer_Start_Sequence marking the character offset where the answer begins (0 through 64 across the file), and an Answers string that re-packages both into SQuAD-compatible form.

The passages are short - 96 to 358 characters - and the questions shorter still at 41 to 87, so nothing here approaches document scale. Topics cluster around the clauses consumers actually ask about: maximum entry age limits beyond which new enrollments are refused, waiting periods before pre-existing conditions are covered, and the process for adding dependents to a policy. The source file is a single CSV running 36,470 bytes, which the Hub auto-converts to Parquet for tabular viewing.

One honest gap: the README card is empty, so the upstream provenance of the policy text is undocumented. Treat the passages as anonymous boilerplate until someone proves otherwise - useful for structure, not citable as any specific carrier's contract language.

Three of the forty-eight rows from the August 2026 research pass, shown exactly as the table arrives:

Context              : Health insurance policies may have a maximum entry age limit, beyond which new enrollments are not accepted. However, existing policyholders may be...
Question             : Is there a maximum age limit to avail this policy?
Answer               : Some policies may have a maximum entry age limit, beyond which new enrollments are not acc...
Answer_Start_Sequence: 5
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Context              : Pre-existing conditions may be covered after a waiting period as specified in the policy....
Question             : Are pre-existing conditions covered after a waiting period?
Answer               : Pre-existing conditions may be covered after a waiting period as specified in the policy....
Answer_Start_Sequence: 0
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Context              : The process for adding dependents to the policy may involve submitting a request to the ins...
Question             : What is the process for adding dependents to the policy?
Answer               : The process for adding dependents to the policy may involve submitting a request to the ins...
Answer_Start_Sequence: 4

These are real rows, not illustrations. Each proves the two properties that make this table useful: answers are cut verbatim from their passages, and the integer offset turns span scoring into arithmetic - a predicted span either lands at Answer_Start_Sequence or it does not. Get a sample and the full 48-row table lands in exactly this shape.

Five fields define the entire table, and all five were verified directly during the August 2026 research pass rather than inferred from documentation - the README behind this dataset is empty, which makes the verified dictionary below the authoritative description of its structure. Three fields carry the question-answer payload (Context, Question, Answer), one carries the offset arithmetic that makes span evaluation mechanical (Answer_Start_Sequence, int64, 0-64), and one exists purely to satisfy SQuAD-shaped tooling (Answers, a serialized dict restating the span and its offset).

That fifth column is worth naming because it is the only redundancy in the file: anything that reads SQuAD-format JSON wants the serialized string, while anything doing plain tabular work wants the flat pair. Decide which consumer you are and drop the other, or ask us to deliver both parsed and byte-faithful versions side by side.

Geography - not attributed. The passages use generic policy wording with no state, country or carrier markers, so jurisdiction has to come from your side of the join rather than from the data.

Temporal - a single static snapshot dating to April 2024, with no per-row issue dates. There is no internal clock in this table; time-series work needs an external one.

Granularity - one question-answer pair per row over short policy passages: clause-level resolution rather than document-level. That narrowness is the point for QA work - each row is a self-contained reading-comprehension item - and the ceiling for anything wanting breadth. Forty-eight rows cover forty-eight clauses, not a policy landscape.

API, files, or your warehouse. Daily, weekly, or hourly.

Your cadence is decoupled from the snapshot's stillness - take a one-off pull of the 48 rows for a chatbot sprint, or keep a landing table current so downstream QA fixtures diff cleanly between builds. Deliveries arrive normalized to the five-column dictionary above: the Answers string either parsed into structured form or preserved byte-for-byte, your call, documented alongside the sample so the choice survives into your storage layer.

• Insurance chatbot prototyping - real policy-question shapes for prompt-format prototyping before any corpus budget gets spent.
• QA pipeline smoke tests - 48 known rows with exact offsets make a loud, fast regression suite; a broken span extractor fails immediately instead of quietly degrading on a large eval set.
• Schema template for larger builds - the five-column layout is the blueprint for scaling to thousands of policy clauses with the same tooling expectations.
• Span-extraction evaluation - offsets make scoring mechanical: predicted span versus recorded start position, no human adjudication needed.
• Prompt-regression fixtures - frozen rows catch behavior drift when models, prompts or preprocessing change underneath a shipping assistant.
• Policy-clause taxonomy seeding - entry age limits, pre-existing-condition waiting periods and dependent-addition procedures form a starter label set for clause classification.

Data scientists and ML engineers lead at 3/3 relevance: this is native NLP material with evaluation-grade offsets built in; see data scientists. Developers and builders match them at 3/3 - 48 deterministic rows slot straight into CI fixtures and landing schemas; see developers and builders. Competitive intel and product teams land at 2/3, specifically conversational-product teams prototyping insurance assistants; see competitive intel product teams. Journalists and academics sit at 2/3: a clean worked example of extractive-QA structure, cited with the empty-README provenance caveat attached; see journalists and academics. Market researchers and consultants rate 1/3 here - 48 rows answer questions about policy wording, not market size; see market researchers. Investors and quant researchers likewise 1/3; see investors and quants.

Within managed health care data, this record owns the narrowest and most literal position: one uploaded table, 48 rows, fixed schema. Its closest sibling, the Hugging Face health insurance dataset search (hugging-face-health-insurance-dataset-search), sweeps whatever the Hub's search engine matches for 'health insurance' - recall where this page gives one specific, verified artifact. The KFF state health policy indicators record (kff-state-health-policy-medicaid-chip-indicators) answers program-level questions with roughly 209 pre-computed state measures, when the deliverable is a ratio rather than a reading-comprehension row. NCQA health plan report cards (ncqa-health-plan-report-cards) grade plan quality where this table describes clause wording. If your question is 'what does an extractive-QA row for insurance policy language actually look like', this is the record; if it is 'what does the market look like', it is not.

Three things worth knowing upfront. First, size honesty: 48 rows and 38.8 kilobytes make this a fixture and a template, not a training corpus - anyone calling it model-training data is selling something. If you need volume, we extend the identical schema to production row counts. Second, the duplicate encoding: the Answers string restates Answer plus Answer_Start_Sequence, so tell us whether your consumers want it parsed or byte-faithful and the delivery reflects that. Third, provenance: the upstream README is empty and the passages carry no jurisdiction or carrier markers, so do not republish them as any specific insurer's contract language. Name your fields, normalization and target volume, and the sample lands in exactly the schema shown above.