> ## Documentation Index > Fetch the complete documentation index at: https://docs.zerogpu.ai/llms.txt > Use this file to discover all available pages before exploring further. # PII > Multilingual PII detection, redaction, and schema-driven extraction models. PII models detect, redact, and extract personally identifiable information and other structured entities from text. Both models below run on CPU for privacy-preserving workflows and select a behavior through the `metadata.usecase` field. Each request is shown for both the [Responses API](/api-reference/responses) and the OpenAI-compatible [Chat Completions API](/api-reference/chat-completions). | Model | Input /1M | Output /1M | Params | Max tokens | | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------: | ---------: | ------ | ---------: | | gliner-multi-pii-v1 gliner-multi-pii-v1 | \$0.02 | \$0.05 | 300M | 800 | | gliner2-base-v1 gliner2-base-v1 | \$0.02 | \$0.05 | 205M | 800 | ## gliner-multi-pii-v1 > 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](https://github.com/fastino-ai/GLiNER2/blob/main/README.md) • [Terms](https://github.com/fastino-ai/GLiNER2/blob/main/LICENSE) • [Privacy](https://github.com/fastino-ai/GLiNER2/blob/main/LICENSE) ### Redaction Set `metadata.usecase` to `redact` and `metadata.mask` to `label` to replace detected PII in place with `[LABEL]` placeholders. ```bash Responses API theme={null} curl https://api.zerogpu.ai/v1/responses \ -H "content-type: application/json" \ -H "x-api-key: YOUR_API_KEY" \ -H "x-project-id: YOUR_PROJECT_ID" \ -d '{ "model": "gliner-multi-pii-v1", "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 will respond as soon as possible.", "metadata": { "mask": "label", "usecase": "redact" } }' ``` ```bash Chat Completions theme={null} curl https://api.zerogpu.ai/v1/chat/completions \ -H "content-type: application/json" \ -H "x-api-key: YOUR_API_KEY" \ -H "x-project-id: YOUR_PROJECT_ID" \ -d '{ "model": "gliner-multi-pii-v1", "messages": [ { "role": "user", "content": "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 will respond as soon as possible." } ], "metadata": { "mask": "label", "usecase": "redact" } }' ``` ```json Response theme={null} { "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"] } } ``` ### Extraction Set `metadata.usecase` to `extract-pii` to detect PII without modifying the source text. Narrow results with `categories` and a confidence `threshold`. ```bash Responses API theme={null} curl https://api.zerogpu.ai/v1/responses \ -H "content-type: application/json" \ -H "x-api-key: YOUR_API_KEY" \ -H "x-project-id: YOUR_PROJECT_ID" \ -d '{ "model": "gliner-multi-pii-v1", "input": "Contact John Doe at john@example.com or +1-415-555-0134.", "metadata": { "usecase": "extract-pii", "threshold": 0.5, "categories": [ "identity", "contact" ] } }' ``` ```bash Chat Completions theme={null} curl https://api.zerogpu.ai/v1/chat/completions \ -H "content-type: application/json" \ -H "x-api-key: YOUR_API_KEY" \ -H "x-project-id: YOUR_PROJECT_ID" \ -d '{ "model": "gliner-multi-pii-v1", "messages": [ { "role": "user", "content": "Contact John Doe at john@example.com or +1-415-555-0134." } ], "metadata": { "usecase": "extract-pii", "threshold": 0.5, "categories": [ "identity", "contact" ] } }' ``` ```json Response theme={null} { "entities": [ { "text": "John Doe", "label": "person", "start": 8, "end": 16, "score": 0.9989 }, { "text": "john@example.com", "label": "email", "start": 20, "end": 36, "score": 0.9776 }, { "text": "+1-415-555-0134", "label": "phone number", "start": 40, "end": 55, "score": 0.9714 } ], "entities_by_label": { "person": ["John Doe"], "email": ["john@example.com"], "phone number": ["+1-415-555-0134"] } } ``` ## gliner2-base-v1 > gliner2-base-v1 is a versatile extraction-and-classification model for the structured tasks that fill most production pipelines. Point it at any text and, with a single API call, pull named entities by your own labels, populate a typed JSON schema straight from messy input, or classify by sentiment, intent, or topic. No fine-tuning and no prompt engineering, just a label set or schema at inference time. Because it's purpose-built and CPU-optimized, it runs faster and cheaper than routing this work to a general-purpose frontier model. Reach for gliner-multi-pii-v1 when the job is dedicated PII redaction. When you need clean structure out of raw text, this is the model. **References:** [Model docs](https://github.com/fastino-ai/GLiNER2/blob/main/README.md) • [Terms](https://github.com/fastino-ai/GLiNER2/blob/main/LICENSE) • [Privacy](https://github.com/fastino-ai/GLiNER2/blob/main/LICENSE) ### Entity extraction Set `metadata.usecase` to `ner` and pass your own `labels` to extract named entities. ```bash Responses API theme={null} curl https://api.zerogpu.ai/v1/responses \ -H "content-type: application/json" \ -H "x-api-key: YOUR_API_KEY" \ -H "x-project-id: YOUR_PROJECT_ID" \ -d '{ "model": "gliner2-base-v1", "input": "The application is built with Python 3.11 and uses PostgreSQL 15 for storage. It runs on Kubernetes with Docker containers and communicates via gRPC.", "metadata": { "labels": [ "programming language", "database", "technology", "protocol" ], "usecase": "ner", "threshold": 0.3 } }' ``` ```bash Chat Completions theme={null} curl https://api.zerogpu.ai/v1/chat/completions \ -H "content-type: application/json" \ -H "x-api-key: YOUR_API_KEY" \ -H "x-project-id: YOUR_PROJECT_ID" \ -d '{ "model": "gliner2-base-v1", "messages": [ { "role": "user", "content": "The application is built with Python 3.11 and uses PostgreSQL 15 for storage. It runs on Kubernetes with Docker containers and communicates via gRPC." } ], "metadata": { "labels": [ "programming language", "database", "technology", "protocol" ], "usecase": "ner", "threshold": 0.3 } }' ``` ```json Response theme={null} { "entities": { "programming language": ["Python 3.11"], "database": ["PostgreSQL 15"], "technology": ["Kubernetes", "Docker", "gRPC"], "protocol": ["gRPC"] } } ``` ### Structured JSON extraction Set `metadata.usecase` to `json` and define a `schema` of `field::type::description` entries to pull structured records out of free text. ```bash Responses API theme={null} curl https://api.zerogpu.ai/v1/responses \ -H "content-type: application/json" \ -H "x-api-key: YOUR_API_KEY" \ -H "x-project-id: YOUR_PROJECT_ID" \ -d '{ "model": "gliner2-base-v1", "input": "Best regards, John Smith, Senior Software Engineer at Acme Corp. Phone: (555) 123-4567, Email: john.smith@acme.com, Office: 123 Main Street, Suite 400, San Francisco, CA 94105", "metadata": { "schema": { "contact": [ "name::str::Full name", "title::str::Job title", "company::str::Company name", "phone::str::Phone number", "email::str::Email address", "address::str::Office address" ] }, "usecase": "json" } }' ``` ```bash Chat Completions theme={null} curl https://api.zerogpu.ai/v1/chat/completions \ -H "content-type: application/json" \ -H "x-api-key: YOUR_API_KEY" \ -H "x-project-id: YOUR_PROJECT_ID" \ -d '{ "model": "gliner2-base-v1", "messages": [ { "role": "user", "content": "Best regards, John Smith, Senior Software Engineer at Acme Corp. Phone: (555) 123-4567, Email: john.smith@acme.com, Office: 123 Main Street, Suite 400, San Francisco, CA 94105" } ], "metadata": { "schema": { "contact": [ "name::str::Full name", "title::str::Job title", "company::str::Company name", "phone::str::Phone number", "email::str::Email address", "address::str::Office address" ] }, "usecase": "json" } }' ``` ```json Response theme={null} { "data": { "contact": [ { "name": "John Smith", "title": "Senior Software Engineer", "company": "Acme Corp", "phone": "(555) 123-4567", "email": "john.smith@acme.com", "address": null } ] } } ``` ### Classification Set `metadata.usecase` to `classification` and supply a `schema` mapping each axis to its allowed labels. ```bash Responses API theme={null} curl https://api.zerogpu.ai/v1/responses \ -H "content-type: application/json" \ -H "x-api-key: YOUR_API_KEY" \ -H "x-project-id: YOUR_PROJECT_ID" \ -d '{ "model": "gliner2-base-v1", "input": "I absolutely love this product! The quality is outstanding and the customer service was incredibly helpful.", "metadata": { "schema": { "sentiment": [ "positive", "negative", "neutral" ] }, "usecase": "classification" } }' ``` ```bash Chat Completions theme={null} curl https://api.zerogpu.ai/v1/chat/completions \ -H "content-type: application/json" \ -H "x-api-key: YOUR_API_KEY" \ -H "x-project-id: YOUR_PROJECT_ID" \ -d '{ "model": "gliner2-base-v1", "messages": [ { "role": "user", "content": "I absolutely love this product! The quality is outstanding and the customer service was incredibly helpful." } ], "metadata": { "schema": { "sentiment": [ "positive", "negative", "neutral" ] }, "usecase": "classification" } }' ``` ```json Response theme={null} { "classification": { "sentiment": "positive" } } ```