running-zeroshot-ner
Extract arbitrary, custom entity types from clinical or biomedical text with no fine-tuning using OpenMed's GLiNER / GLiNER2 zero-shot support. Use when the user wants to define their own labels on the fly (e.g. Drug, Symptom, Device, Procedure), has no labelled data or a label set not covered by a fine-tuned model, or asks about openmed zero deps/index/infer, the gliner extra, or GLiNER. Pairs adjacent to extracting-clinical-entities (use that for high-accuracy fixed-schema NER) and loading-openmed-models.
Profile →npx skills add maziyarpanahi/openmed --skill running-zeroshot-ner --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Running Zero-Shot NER Zero-shot NER lets you extract entity types you **name at inference time** — no training, no labelled data. OpenMed wraps GLiNER (v1) and GLiNER2 behind a small index + inference layer, exposed via the `openmed zero` CLI and the `openmed.ner` Python API. It runs on-device. ## When to use - Your label set is **custom or evolving** ("Device", "Implant", "Allergen") and no fine-tuned OpenMed model emits exactly those labels. - You have **no labelled data** to fine-tune with. - You need a quick prototype or a one-off extraction over an unusual schema. **When to prefer a fine-tuned model instead** (`extracting-clinical-entities`): for a fixed, well-supported schema (diseases, drugs, anatomy), a fine-tuned OpenMed model is more accurate and faster than zero-shot. Zero-shot trades some accuracy for total label flexibility — use it for coverage of new types, then graduate to a fine-tuned model once the schema stabilises. ## Install ```bash pip install "openmed[gliner]" # pulls GLiNER (and GLiNER2 if a recent gliner is installed) openmed zero deps # diagnostic: prints "GLiNER v1: ok" / "GLiNER v2: ok" ``` `openmed zero deps` only **checks** availability — it does not
- When to use
- Install
- The two-step workflow: index, then infer
- Python API
- Writing good labels
- Hand-off to / from OpenMed
- Edge cases & gotchas
- Standards & references
pip install "openmed[gliner]" # pulls GLiNER (and GLiNER2 if a recent gliner is installed) openmed zero deps # diagnostic: prints "GLiNER v1: ok" / "GLiNER v2: ok" openmed zero index /models/gliner --output /models/gliner/index.json openmed zero infer "Patient on insulin glargine via an insulin pump for type 1 diabetes." \
What does the running-zeroshot-ner skill do?
Extract arbitrary, custom entity types from clinical or biomedical text with no fine-tuning using OpenMed's GLiNER / GLiNER2 zero-shot support. Use when the user wants to define their own labels on the fly (e.g. Drug, Symptom, Device, Procedure), has no labelled data or a label set not covered by a fine-tuned model, or asks about openmed zero deps/index/infer, the gliner extra, or GLiNER. Pairs adjacent to extracting-clinical-entities (use that for high-accuracy fixed-schema NER) and loading-openmed-models.
How do I install it?
Run `npx skills add maziyarpanahi/openmed --skill running-zeroshot-ner --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From maziyarpanahi/openmed, a repository with 4,851 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.