curl --request POST \
--url https://api.zerogpu.ai/v1/responses \
--header 'Content-Type: application/json' \
--header 'x-api-key: <api-key>' \
--data '
{
"input": "Hello Jane Doe, this is John Doe reaching out regarding my recent order. If you need any additional details, feel free to call me at 415-555-0134 during business hours. You can also email me at hi@example.com, and I’ll respond as soon as possible. Looking forward to your update on the issue.",
"model": "gliner-multi-pii-v1"
}
'{
"redacted_text": "Hello [PERSON], this is [PERSON] reaching out regarding my recent order. If you need any additional details, feel free to call me at [PHONE_NUMBER] during business hours. You can also email me at [EMAIL], and I will respond as soon as possible.",
"entities": [
{
"text": "Jane Doe",
"label": "person",
"start": 6,
"end": 14,
"score": 0.9982
},
{
"text": "John Doe",
"label": "person",
"start": 24,
"end": 32,
"score": 0.9981
},
{
"text": "415-555-0134",
"label": "phone number",
"start": 133,
"end": 145,
"score": 0.9725
},
{
"text": "hi@example.com",
"label": "email",
"start": 194,
"end": 208,
"score": 0.9812
}
],
"entities_by_label": {
"person": [
"Jane Doe",
"John Doe"
],
"phone number": [
"415-555-0134"
],
"email": [
"hi@example.com"
]
}
}gliner-multi-pii-v1
Model details for gliner-multi-pii-v1.
curl --request POST \
--url https://api.zerogpu.ai/v1/responses \
--header 'Content-Type: application/json' \
--header 'x-api-key: <api-key>' \
--data '
{
"input": "Hello Jane Doe, this is John Doe reaching out regarding my recent order. If you need any additional details, feel free to call me at 415-555-0134 during business hours. You can also email me at hi@example.com, and I’ll respond as soon as possible. Looking forward to your update on the issue.",
"model": "gliner-multi-pii-v1"
}
'{
"redacted_text": "Hello [PERSON], this is [PERSON] reaching out regarding my recent order. If you need any additional details, feel free to call me at [PHONE_NUMBER] during business hours. You can also email me at [EMAIL], and I will respond as soon as possible.",
"entities": [
{
"text": "Jane Doe",
"label": "person",
"start": 6,
"end": 14,
"score": 0.9982
},
{
"text": "John Doe",
"label": "person",
"start": 24,
"end": 32,
"score": 0.9981
},
{
"text": "415-555-0134",
"label": "phone number",
"start": 133,
"end": 145,
"score": 0.9725
},
{
"text": "hi@example.com",
"label": "email",
"start": 194,
"end": 208,
"score": 0.9812
}
],
"entities_by_label": {
"person": [
"Jane Doe",
"John Doe"
],
"phone number": [
"415-555-0134"
],
"email": [
"hi@example.com"
]
}
}GLiNER Multi PII is a multilingual PII detection and redaction model that supports on-prem deployments as well. It identifies 40+ personally identifiable entity types — identity, contact, government IDs, financial, medical and more. It works natively across six languages: English, French, German, Spanish, Italian, and Portuguese, so a single model covers multi-market and cross-border data without separate per-language pipelines. Built for zero-shot label, it accepts custom label sets at inference time and supports curated PII catalogues, redaction with label or character masks, and generic NER with user-supplied labels.References: Model docs • Terms • Privacy
Authorizations
Headers
Optional project identifier. Scopes the request to a specific project when provided.
Body
Model identifier (fixed for this playground). Use request examples to change use cases.
"gliner-multi-pii-v1""gliner-multi-pii-v1"
Multi-line text or document content to send to the model.
1 - 131072Use-case options for the model. Two use cases are supported:
extract-pii— return detected PII entities without altering the source text.redact— mask detected PII in-line in the returnedredacted_text(e.g.[PERSON],[EMAIL]).
Hide child attributes
Hide child attributes
Which operation to run. extract-pii returns only the detected entities; redact also returns a masked redacted_text.
extract-pii, redact Redaction style, used when usecase is redact. label replaces each entity with a typed placeholder such as [PERSON] or [EMAIL].
label Minimum confidence score (0–1) an entity must reach to be returned. Lower values surface more, lower-confidence matches.
0 <= x <= 1Restrict detection to specific PII category groups (e.g. identity, contact). Omit to detect all supported categories.
["identity", "contact"]
Response
Success
The response is of type object.

