Text Classification

Text classification models map a passage to predefined labels or categories. ZeroGPU offers IAB taxonomy classifiers tuned for ad tech, a general zero-shot classifier where you supply the candidate labels, and the schema-based GLiNER2.5 models, which classify, extract entities, fill a JSON schema, and make dynamic-label decisions such as intent and routing from the same request. Each request is shown for both the Responses API and the OpenAI-compatible Chat Completions API.

Model	Input /1M	Output /1M	Max tokens
`zlm-v1-iab-classify-edge`	$0.02	$0.05	400
`zlm-v2-iab-classify-edge-enriched`	$0.05	$0.15	800
`zlm-v1-iab-domain-classifier`	$0.02	$0.05	100
`deberta-v3-small`	$0.02	$0.05	400
`zlm-v1-signal-extract`	$0.04	$0.10	400
`gliner2.5-base-v1`	$0.02	$0.05	—
`gliner2.5-multi-v1`	$0.02	$0.05	—
`gliner2.5-decide-v1`	$0.004	Not billed	—

zlm-v1-iab-classify-edge

ZeroGPU’s IAB classifier maps any text to the industry-standard IAB Content Taxonomy in a single, fast inference call. Each call returns categories across both the 1.0 and 2.2 taxonomies plus matched audience segments, every result scored for confidence. With support for 50+ languages, it classifies multilingual content without a translation step. At 90M parameters on ONNX, it’s built for the high-volume, sub-millisecond classification that ad tech and content platforms demand, right at the edge. When you need clean category and audience signals on every request, this is the model — and when you need the full profile, reach for the enriched variant.

References: Model docs • Terms • Privacy

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 '{
  "input": "Technology has quietly reshaped the rhythm of everyday life, weaving itself into routines so seamlessly that it often goes unnoticed. From the moment a smartphone alarm wakes someone in the morning to the final glance at a glowing screen before sleep, digital systems guide communication, navigation, work, and entertainment. This transformation did not happen overnight. It emerged through decades of incremental innovation, each new tool building upon the last, until the modern world became deeply interconnected.One of the most significant changes has been the speed at which information travels.",
  "model": "zlm-v1-iab-classify-edge"
}'

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": "zlm-v1-iab-classify-edge",
  "messages": [
    {
      "role": "user",
      "content": "What are the basics of exercise and physical fitness?Exercise is anything that gets your body moving. Regular exercise is one of the best things you can do for your health. It has many benefits, including improving your overall health and fitness, and reducing your risk for many chronic (long-term) diseases.Every physical fitness routine is built on a few simple ideas. These include:Make exercise a habit, as your body adapts to the type of activity you do most often. Regular practice will help you improve.Build up your activity level slowly to help you continue to get stronger, faster, or more flexible without pushing too hard all at once.Challenge yourself by lifting slightly heavier weights, adding a few more minutes to your walk, or increasing your pace."
    }
  ]
}'

Returns IAB audience categories plus content matches across taxonomy versions, each with a confidence score.

Response

{
  "audience": [
    { "name": "Technology & Computing", "score": 0.7686 },
    { "name": "Consumer Electronics", "score": 0.7206 },
    { "name": "65-69", "score": 0.5950 }
  ],
  "content": {
    "iab_1_0": [
      { "name": "Cell Phones", "score": 0.7597 },
      { "name": "Technology & Computing", "score": 0.7387 }
    ],
    "iab_2_2": [
      { "name": "Smartphones", "score": 0.7597 },
      { "name": "Wearable Technology", "score": 0.7387 },
      { "name": "Technology & Computing", "score": 0.7109 }
    ]
  }
}

zlm-v2-iab-classify-edge-enriched

The enriched variant of ZeroGPU’s IAB classifier turns a single inference call into a full content-intelligence profile not just a label, but everything a contextual pipeline needs to act on. Each call returns IAB categories across both the 1.0 and 2.2 taxonomies (down to tier-3), audience and interest segments, topics, keywords, and user-intent classification — all with confidence scores with edge-native, sub-millisecond speeds. With support for 50+ languages, it delivers the same full profile on multilingual content without a translation step. Built for contextual ad targeting, brand-safety scoring, publisher content categorization, and signal pipelines that need full metadata on every request.

References: Model docs • Terms • Privacy

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 '{
  "input": "Technology has quietly reshaped the rhythm of everyday life, weaving itself into routines so seamlessly that it often goes unnoticed. From the moment a smartphone alarm wakes someone in the morning to the final glance at a glowing screen before sleep, digital systems guide communication, navigation, work, and entertainment. This transformation did not happen overnight. It emerged through decades of incremental innovation, each new tool building upon the last, until the modern world became deeply interconnected.One of the most significant changes has been the speed at which information travels.",
  "model": "zlm-v2-iab-classify-edge-enriched"
}'

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": "zlm-v2-iab-classify-edge-enriched",
  "messages": [
    {
      "role": "user",
      "content": "What are the basics of exercise and physical fitness?Exercise is anything that gets your body moving. Regular exercise is one of the best things you can do for your health. It has many benefits, including improving your overall health and fitness, and reducing your risk for many chronic (long-term) diseases.Every physical fitness routine is built on a few simple ideas. These include:Make exercise a habit, as your body adapts to the type of activity you do most often. Regular practice will help you improve.Build up your activity level slowly to help you continue to get stronger, faster, or more flexible without pushing too hard all at once.Challenge yourself by lifting slightly heavier weights, adding a few more minutes to your walk, or increasing your pace."
    }
  ]
}'

In addition to scored audience and content categories, the enriched model returns taxonomy codes and parent IDs, plus topics, keywords, and an inferred user_intent.

Response

{
  "audience": [
    {
      "id": 687,
      "parent_id": 206,
      "name": "Technology & Computing",
      "tier1_name": "Interest",
      "tier2_name": "Technology & Computing",
      "score": 0.7686
    },
    {
      "id": 703,
      "parent_id": 687,
      "name": "Consumer Electronics",
      "tier1_name": "Interest",
      "tier2_name": "Technology & Computing",
      "tier3_name": "Consumer Electronics",
      "score": 0.7206
    }
  ],
  "content": {
    "iab_1_0": [
      { "code": "IAB19-6", "name": "Cell Phones", "tier": 2, "parent_code": "IAB19", "score": 0.7597 },
      { "code": "IAB19", "name": "Technology & Computing", "tier": 1, "parent_code": null, "score": 0.7387 }
    ]
  },
  "topics": [
    { "name": "smartphones", "score": 0.7597 },
    { "name": "wearable technology", "score": 0.7387 },
    { "name": "technology & computing", "score": 0.7109 }
  ],
  "keywords": ["technology", "reshaped", "everyday", "routines", "quietly", "weaving", "rhythm", "itself", "life", "into"],
  "user_intent": {
    "name": "mastering technology quietly reshaped rhythm everyday life",
    "category": "informational",
    "score": 0.5
  }
}

zlm-v1-iab-domain-classifier

ZeroGPU’s Domain IAB Classifier maps a raw domain name straight to the IAB Content Taxonomy in a single, fast inference call, returning content categories, topics, keywords, and user-intent signals. Where the page-level IAB classifiers take the full body of an article as input, this model takes only the domain (for example nytimes.com), reducing payload size by up to 10x while still surfacing the contextual signals that bidstream enrichment, contextual targeting, and domain-level intelligence pipelines run on. At 149M parameters on ONNX, it’s built for the high-volume, sub-millisecond classification that ad tech and content platforms demand, right at the edge.

References: Model docs • Terms • Privacy

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 '{
  "input": "nytimes.com",
  "model": "zlm-v1-iab-domain-classifier"
}'

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": "zlm-v1-iab-domain-classifier",
  "messages": [
    {
      "role": "user",
      "content": "nytimes.com"
    }
  ]
}'

Returns scored content categories across taxonomy versions plus topics, keywords, and an inferred user_intent for the domain.

Response

{
  "content": {
    "iab_1_0": [
      { "name": "News", "score": 0.8124 },
      { "name": "Politics", "score": 0.6533 }
    ],
    "iab_2_2": [
      { "name": "News and Politics", "score": 0.8124 },
      { "name": "Business and Finance", "score": 0.5980 }
    ]
  },
  "topics": [
    { "name": "news", "score": 0.8124 },
    { "name": "journalism", "score": 0.6740 },
    { "name": "current events", "score": 0.6533 }
  ],
  "keywords": ["news", "politics", "world", "business", "opinion", "media", "journalism"],
  "user_intent": {
    "name": "reading news and current events",
    "category": "informational",
    "score": 0.5
  }
}

deberta-v3-small

Microsoft’s DeBERTa-v3-small is a fast, lightweight zero-shot text classifier for high-volume routing, filtering, and tagging. Hand it any text alongside your own candidate labels and it returns a confidence score for each. No training data and no fixed taxonomy, just the categories you define at request time. Because it scores every label rather than forcing one choice, you get soft probabilities to threshold, rank, or route on however your pipeline needs. Purpose-built and CPU-optimized, it runs faster and cheaper than asking a general-purpose frontier model to classify.

References: Model docs • Terms • Privacy

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": "deberta-v3-small",
  "input": "Apple is expected to unveil its next-generation M5 chip at WWDC this June, promising a 40% boost in GPU performance and a new dedicated AI core for on-device machine learning tasks.",
  "instructions": "[sports, finance, politics, technology]"
}'

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": "deberta-v3-small",
  "messages": [
    {
      "role": "system",
      "content": "[sports, finance, politics, technology]"
    },
    {
      "role": "user",
      "content": "Apple is expected to unveil its next-generation M5 chip at WWDC this June, promising a 40% boost in GPU performance and a new dedicated AI core for on-device machine learning tasks."
    }
  ]
}'

Returns each candidate label with a probability score.

Response

{
  "technology": 0.971784,
  "politics": 0.011858,
  "sports": 0.01175,
  "finance": 0.004608
}

zlm-v1-signal-extract

ZeroGPU’s signal extractor turns unstructured text into structured signals that downstream systems can act on. One inference call returns topics, keywords, intent, and other contextual attributes as structured output, so content enrichment, contextual intelligence, ad targeting, agent routing, recommendation systems, and analytics pipelines all read from the same pass. At 80M parameters it is built for high-volume workloads where a general-purpose LLM is more machinery than the job needs.

References: Terms • Privacy Pass the signal categories you want in categories; max_keywords caps how many keywords come back.

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": "zlm-v1-signal-extract",
  "input": "How can I pay using my credit card?",
  "categories": [
    "signals"
  ]
}'

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": "zlm-v1-signal-extract",
  "messages": [
    {
      "role": "user",
      "content": "How can I pay using my credit card?"
    }
  ],
  "categories": [
    "signals"
  ],
  "max_keywords": 6
}'

The signals arrive as a JSON string in the output text. Parsed:

Response

{
  "keywords": [
    "credit card",
    "login",
    "official website",
    "account access"
  ],
  "user_intent": {
    "name": "pay using credit card",
    "category": "navigational",
    "score": 0.95
  }
}

gliner2.5-base-v1

Fastino’s GLiNER2.5 Base is an English schema-based information extraction model built on DeBERTa-v3-base. A single model covers entity extraction, text classification, structured record extraction, relation extraction, and span-level attributes — you supply a label set or a schema at inference time instead of fine-tuning one model per task. It fits GTM workflows such as parsing inbound leads, extracting company and contact details, classifying sales conversations, identifying relationships between people and companies, structuring CRM notes, and enriching customer or account data.

References: Model docs • Terms • Privacy

Entity Extraction

Set metadata.usecase to ner and pass your own labels to extract named entities. threshold sets the minimum confidence for a span to be returned.

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 '{
  "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.",
  "model": "gliner2.5-base-v1",
  "metadata": {
    "labels": [
      "programming language",
      "database",
      "technology",
      "protocol"
    ],
    "usecase": "ner",
    "threshold": 0.3
  }
}'

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": "gliner2.5-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
  }
}'

Structured Data Extraction

Set metadata.usecase to json and define a schema of field::type::description entries to pull structured records out of free text.

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 '{
  "input": "Best regards, John Smith, Senior Software Engineer at Acme Corp. Phone: (555) 123-4567, Email: [email protected], Office: 123 Main Street, Suite 400, San Francisco, CA 94105",
  "model": "gliner2.5-base-v1",
  "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"
  }
}'

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": "gliner2.5-base-v1",
  "messages": [
    {
      "role": "user",
      "content": "Best regards, John Smith, Senior Software Engineer at Acme Corp. Phone: (555) 123-4567, Email: [email protected], 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"
  }
}'