GLiNER PII and NuNER Zero

Two open models that find the personal data in a text, in one API call. superlinked/pii-redact

Accuracy

SPIA, F1 · not measured as one model
Time per request

61 ms

per document
Serverless price

$0.08

per 1M tokens

Leave 32% less personal data exposed than AWS Comprehend, 79% cheaper

90.9%

Personal data masked · 660 documents

61 ms

Time per document

$73

Cost per 1M documents

See the comparison on Redact (PII)

Call it

from sie_sdk import SIEClient
client = SIEClient(api_key="sk-sie-…")
result = client.extract(
    "modelsuperlinked/pii-redact",
    {"text": "text{"date_time": "1978-03-29 17:28:13", "name": "Emma A. Platini", "street_address": "19325 John Motorway, Apt. 488"}
{"date_time": "2022-01-02 14:35:18", "name": "John D. Smith", "street_address": "456 Oakwood Avenue, Unit 3C"}
{"date_time": "2021-12-15 09:19:21", "name": "Alex N. Thompson", "street_address": "334 King's Cross, Flat 15B"}"},
)
for span in result["entities"]:
    print(span["label"], span["text"])

import { SIEClient } from '@superlinked/sie-sdk';

const client = new SIEClient(undefined, { apiKey: 'sk-sie-…' });

const result = await client.extract(
  'superlinked/pii-redact',
  { text: "{\"date_time\": \"1978-03-29 17:28:13\", \"name\": \"Emma A. Platini\", \"street_address\": \"19325 John Motorway, Apt. 488\"}\n{\"date_time\": \"2022-01-02 14:35:18\", \"name\": \"John D. Smith\", \"street_address\": \"456 Oakwood Avenue, Unit 3C\"}\n{\"date_time\": \"2021-12-15 09:19:21\", \"name\": \"Alex N. Thompson\", \"street_address\": \"334 King's Cross, Flat 15B\"}" },
  { labels: [] }, // the model's default types
);
console.log(result.entities);

curl https://api.superlinked.com/v1/extract/superlinked%2Fpii-redact \
  -H "Authorization: Bearer sk-sie-…" \
  -H "Content-Type: application/json" \
  -d "{\"items\":[{\"text\":\"{\\\"date_time\\\": \\\"1978-03-29 17:28:13\\\", \\\"name\\\": \\\"Emma A. Platini\\\", \\\"street_address\\\": \\\"19325 John Motorway, Apt. 488\\\"}\\n{\\\"date_time\\\": \\\"2022-01-02 14:35:18\\\", \\\"name\\\": \\\"John D. Smith\\\", \\\"street_address\\\": \\\"456 Oakwood Avenue, Unit 3C\\\"}\\n{\\\"date_time\\\": \\\"2021-12-15 09:19:21\\\", \\\"name\\\": \\\"Alex N. Thompson\\\", \\\"street_address\\\": \\\"334 King's Cross, Flat 15B\\\"}\"}]}"

Build the "Find personal data to mask" capability into my app using the Superlinked Inference Engine (SIE).

Context
- SIE is an OpenAI-style inference API. Python SDK: `from sie_sdk import SIEClient`; TypeScript: `@superlinked/sie-sdk`.
- Base URL: https://api.superlinked.com (or my regional endpoint). Auth: Bearer key from env `SIE_API_KEY` (never hard-code it).
- Model: superlinked/pii-redact (SIE primitive: /extract). Keep the model id configurable.

Task
- Input: a block of text.
- Behaviour: return the spans of personal data found, each with its type and character offsets; my code masks them.
- Call the selected SIE primitive once per request and map the response into your domain type.

Deliverables
- A typed client wrapper, an application-level function for this task, error handling for timeouts/empty input, and unit tests with a stubbed client.
- Wire it into my existing stack (ask me which framework if unclear) and add a short usage example.

Run

Output

entities · 6start–end

person Emma A. Platini 46–61
street address 19325 John Motorway, Apt. 488 83–112
person John D. Smith 161–174
street address 456 Oakwood Avenue, Unit 3C 196–223
person Alex N. Thompson 272–288
street address 334 King's Cross, Flat 15B 310–336

masked · 6

{"date_time": "1978-03-29 17:28:13", "name": "name", "street_address": "address"}
{"date_time": "2022-01-02 14:35:18", "name": "name", "street_address": "address"}
{"date_time": "2021-12-15 09:19:21", "name": "name", "street_address": "address"}

Next to the other PII models

Next to the other PII models
Model	SPIA, F1	Time per request	Serverless price	Size
GLiNER PII and NuNER Zero superlinked	Not measured	61 ms per document	$0.08 per 1M tokens	Not listed
NuNER Zero numind	0.463	Not timed	$0.05 per 1M tokens	449M
gliner multi pii v1 urchade	0.377	Not timed	$0.04 per 1M tokens	289M

All 3 PII models

Facts

Runs: urchade/gliner_multi_pii-v1
numind/NuNER_Zero
Endpoint: extract

Evaluation

Accuracy: Run on SIE against the public dataset named in each figure
Time: SIE timed on a GPU server with the client alongside, one request at a time
Prices: Serverless list prices
Sources: Task page runs and their inputs

Run GLiNER PII and NuNER Zero on Serverless or in your own cloud

SIE Serverless

We host the models; you call one API.

No idle GPUs, pay for what you use
Fits your stack: SDK, API, CLI, MCP
Self-host the same open-source engine
SOC 2 Type 2, US or EU data residency
Create an account to try Serverless

Get started

SIE Cluster

Kubernetes in your own cloud, backed by our engineers.

Terraform modules for your cloud
Apache-2.0, same engine as Serverless
GPU workers scale to zero between jobs
Separate worker pools for each tenant

Agent prompt Deploy SIE to our AWS account with the superlinked/sie/aws Terraform module. Docs: superlinked.com/docs/deployment.

Deploy SIE to our Google Cloud project with the superlinked/sie/google Terraform module. Docs: superlinked.com/docs/deployment.

Deploy SIE to our Azure subscription with the superlinked/sie/azure Terraform module. Docs: superlinked.com/docs/deployment.

Deploy SIE to our Alibaba Cloud account with the superlinked/sie/alicloud Terraform module. Docs: superlinked.com/docs/deployment.

Talk to us Deploy guide

SIE Local

The open-source server on your own machine.

Runs on your NVIDIA GPU
One container, no cluster
Process your data fully offline
Same SDK and model IDs as Serverless

docker run --gpus all -p 8080:8080 ghcr.io/superlinked/sie-server:latest-cuda12-default

Quickstart

Leave 32% less personal data exposed than AWS Comprehend, 79% cheaper

Call it

Next to the other PII models

Facts

Run GLiNER PII and NuNER Zero on Serverless or in your own cloud