AmareshHebbar/insurance-classifier-sft

Insurance Coverage Classifier (Stark Law DHS)

Part of the [AxisMapper Medical AI Suite](https://huggingface.co/collections/AmareshHebbar/axiomapper-medical-ai-suite) — 16 domain-specific SFT datasets for fine-tuning medical LLMs.

Built by [AmareshHebbar](https://huggingface.co/AmareshHebbar) | Studio Ilios / Humanova Minds

What this dataset does

CPT/HCPCS codes → Stark Law DHS classification + compliance notes

Why download this

Compliance automation for physician self-referral rules. Identify which services are Designated Health Services under Stark Law Section 1877. Essential for hospital compliance teams.

Dataset stats

Data format

Every row is a messages list in chat format — compatible with Unsloth, TRL SFTTrainer, LLaMA-Factory, and any OpenAI-style fine-tuning pipeline:

{
  "messages": [
    {"role": "system",    "content": "You are a ..."},
    {"role": "user",      "content": "CPT/HCPCS: 86890
Service: Autologous blood process"},
    {"role": "assistant", "content": "Code: 86890
Stark Law: DESIGNATED HEALTH SERVICE (DHS)
Note: Self-referral restrictions apply under Section 1877. Verify applicable exceptions before billing."}
  ]
}

Data source

CMS HCPCS 2026 Stark Law Designated Health Services code list → https://www.cms.gov/medicare/coding-billing/hcpcs-codes/annual-release

All data is extracted from authoritative public sources. No LLM-generated or synthetic content.

Who should use this

Hospital compliance officers, healthcare attorneys, revenue integrity teams, health system legal departments.

Quick start

from datasets import load_dataset

ds = load_dataset("AmareshHebbar/insurance-classifier-sft")
print(ds["train"][0])

Fine-tuning example (Unsloth)

from unsloth import FastLanguageModel
from trl import SFTTrainer
from datasets import load_dataset

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="unsloth/Qwen2.5-3B-Instruct",
    max_seq_length=2048,
    load_in_4bit=True,
)

dataset = load_dataset("AmareshHebbar/insurance-classifier-sft", split="train")

trainer = SFTTrainer(
    model=model,
    tokenizer=tokenizer,
    train_dataset=dataset,
    dataset_text_field="messages",
    max_seq_length=2048,
)
trainer.train()

Related datasets in this collection

Citation

@misc{axiomapper2026,
  author    = {Hebbar, Amaresh},
  title     = {AxisMapper: Medical AI Fine-tuning Dataset Suite},
  year      = {2026},
  publisher = {HuggingFace},
  url       = {https://huggingface.co/collections/AmareshHebbar/axiomapper-medical-ai-suite}
}

AxisMapper is an open-source project. Star the repo, open issues, and contribute at [GitHub](https://github.com/amareshhebbar/AxisMapper).