AmareshHebbar/medical-ner-sft

Medical Named Entity Recognition (NER)

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

Clinical text → structured JSON with conditions, drugs, dosages, procedures

Why download this

Train clinical NER models to extract structured data from unstructured clinical notes. Output is JSON-formatted for downstream pipeline integration.

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": "Patient was prescribed Metformin 500mg twice daily for type 2 diabetes and Lisinopril 10mg once daily for hypertension."},
    {"role": "assistant", "content": "{\"conditions\": [\"type 2 diabetes\", \"hypertension\"], \"drugs\": [\"Metformin\", \"Lisinopril\"], \"dosages\": [\"500mg twice daily\", \"10mg once daily\"], \"procedures\": []}"}
  ]
}

Data source

Clinical notes from AGBonnet/augmented-clinical-notes — medication, diagnosis, and procedure extraction → https://huggingface.co/datasets/AGBonnet/augmented-clinical-notes

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

Who should use this

Clinical NLP researchers, EHR data extraction engineers, pharmacovigilance teams, health AI startups.

Quick start

from datasets import load_dataset

ds = load_dataset("AmareshHebbar/medical-ner-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/medical-ner-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).