Cookbook: Classify text into your own labels

Classification assigns each text one label per task, from label sets you define. One request can run several tasks at once, such as topic and priority, and the text can be in any supported language.

Request

The message below is Spanish. The labels stay in English.

curl -X POST https://api.fastino.ai/v1/chat/completions \
  -H "Authorization: Bearer $FASTINO_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "fastino/gliner2.5-multi-v1",
  "messages": [
    {
      "role": "user",
      "content": "La aplicación se cierra cada vez que intento pagar con tarjeta. Necesito una solución hoy."
    }
  ],
  "schema": {
    "classifications": [
      {
        "task": "topic",
        "labels": [
          "billing",
          "bug",
          "account",
          "feature_request"
        ],
        "multi_label": false
      },
      {
        "task": "priority",
        "labels": [
          "urgent",
          "routine"
        ],
        "multi_label": false
      }
    ]
  },
  "threshold": 0.5,
  "store": false
}'

Request schema

Field	Type	In this recipe
`model`	string	`fastino/gliner2.5-multi-v1`.
`messages`	array	One `user` message holding the text to classify.
`schema.classifications[].task`	string	Your name for the task. It becomes a key in the result.
`schema.classifications[].labels`	array	The labels to choose from.
`schema.classifications[].multi_label`	boolean	`false` returns exactly one label per task. `true` can return several.
`threshold`	number	Minimum confidence, from 0 to 1. Default `0.5`.
`store`	boolean	Defaults to `true`, which saves the inference. `false` opts out.

Response

GLiNER returns the result as a JSON string in choices[0].message.content. Parsed, it looks like this. Measured against https://api.fastino.ai; confidence values vary slightly between calls.

{
  "topic": {
    "label": "bug",
    "confidence": 0.844
  },
  "priority": {
    "label": "urgent",
    "confidence": 0.938
  }
}

Response schema

Field	Type	Meaning
`<task>`	object	One key per task you defined. Here, `topic` and `priority`.
`<task>.label`	string	The winning label for a single-label task.
`<task>.confidence`	number	Confidence for that label, from 0 to 1.

Tasks are scored together, so adding or removing a task can shift the other tasks’ confidence values slightly.

Act on the answer

import json

result = json.loads(response.json()["choices"][0]["message"]["content"])
topic, priority = result["topic"], result["priority"]

if topic["confidence"] < 0.6:
    route_to("triage")  # let a person choose
else:
    route_to(topic["label"], urgent=priority["label"] == "urgent")

Adapt it

Keep labels mutually exclusive for single-label tasks. Overlapping labels split confidence.
Use multi_label: true for tags that can co-occur, such as product areas mentioned in one message.
Use clear, short label names. Downstream code reads them as values.
For decisions that need rules, scales, or yes or no answers about a situation, use GLiDE instead.

Call GLiNER

Every GLiNER schema type and how to read its result.

Chat Completions API reference

Every field, limit, and error for POST /v1/chat/completions.

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Cookbook: Classify text into your own labels

Request

Request schema

Response

Response schema

Act on the answer

Adapt it

Call GLiNER

Chat Completions API reference

​Request

​Request schema

​Response

​Response schema

​Act on the answer

​Adapt it

Call GLiNER

Chat Completions API reference

Request

Request schema

Response

Response schema

Act on the answer

Adapt it