README.md

metadata

title: GLiNER2.5-multi-Decide — Multilingual Text Decisions
emoji: 🌍
colorFrom: red
colorTo: blue
sdk: gradio
sdk_version: 6.28.0
app_file: app.py
short_description: Multilingual zero-shot text classification, any label set
python_version: '3.12'
startup_duration_timeout: 30m

Multilingual operational text classification with fastino/GLiNER2.5-multi-Decide.

One 287M-parameter multilingual checkpoint (mDeBERTa-v3-base) answers any label set you pass at call time — intent, routing, sentiment, priority, policy, multi-label tags — in a single forward pass. No prompt template, no generated tokens. Use it when the text is not English: the same call scores several decision heads at once, and single-label heads return one string while multi-label heads return every label above their threshold.

How to use it

1. Paste the text to decide about — any language.
2. Edit the decisions table — one row per decision head:
   • Decision — the head's name (intent, sentiment, urgency, …).
   • Labels — comma-separated candidate labels. Use "0", "1", … strings for an ordinal scale.
   • Multi-label — tick when several labels can apply at once (review aspects, topics).
   • Threshold — the confidence cutoff for multi-label heads.
3. Press Decide. Every head is scored together in the same call.

Advanced → Per-decision overrides takes a JSON object merged into a head by name, for a question over the passage (prompt), label descriptions, or other per-head config:

{"answer": {"prompt": "Did the treaty enter into force in 1992?"}}

{"intent": {"labels": {"card_pin_change": "The customer wants a new PIN", "card_lost": "The physical card is missing"}}}

About the model

GLiNER2.5-multi-Decide is the multilingual specialist of the GLiNER2.5 family for operational decisions: customer and banking intent, travel and clinic requests, review sentiment, document type, email and ticket routing, human handoff, moderation, severity, urgency, and spam. It scores 56.7% exact-match on the 17-domain held-out fastino/fast-decisions suite, and is loaded through the gliner2 library's AutoExtractor.

It is not a general-purpose model: it does not reason, explain, or answer open questions.

Links

• Model: fastino/GLiNER2.5-multi-Decide
• Code: github.com/fastino-ai/GLiNER2
• Paper: GLiNER2 (arXiv:2507.18546)

License

Apache-2.0 (model). The example texts are the ones published on the model card.