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Entity extraction is the most common GLiNER 2.5 Multi workload. You name the labels you want in English, send text in any supported language, and get back each match with its exact character offsets. No training is needed.

Request

The text below is German. The labels stay in English.

Request schema

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.

Response schema

Act on the answer

The offsets point back into your original text, so you can highlight or redact matches in place.

Adapt it

  • Write descriptions that separate similar labels, such as company versus government agency. A vague label such as product can pull in near misses like contract names.
  • Raise threshold for precision, or lower it for recall. Tune it on a labeled sample.
  • Keep label names stable. Downstream code reads them as keys.
  • Need labels to be fixed records instead of loose spans? Use schema.structures.

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.