fastino/GLiNER2.5-Decide — new model trending #30 on Hugging Face

The suite is English. Use [`GLiNER2.5-multi-Decide`](https://huggingface.co/fastino/GLiNER2.5-multi-Decide) when the input is multilingual. Domain and label details are on the [dataset card](https://huggingface.co/datasets/fastino/fast-decisions).

This release is not a general-purpose model. It does not reason, explain, or answer open questions. It is a specialist for operational decisions: customer and banking intent, travel and clinic requests, review sentiment, document type, email and ticket routing, human handoff, agent completion, moderation, severity, urgency, and spam. The same call also answers a question about a passage, classifies a book, uses labels that carry a description, and scores an ordinal scale.

Outputs below are potential results for these inputs. They show the shape `classify_text` returns.

## Install

```bash

pip install gliner2

```

```python

from gliner2 import AutoExtractor

model = AutoExtractor.from_pretrained("fastino/GLiNER2.5-Decide")

```

## Examples

### Customer support intent

Route a live message before a human ever sees it. Refunds, cancellations, login failures, and shipping delays share one inbox and one label set. Decide picks the action the queue should take, so the right workflow starts on the first turn.

```python

model.classify_text(

"My subscription renewed on April 15 for ¥5,400 after the service was already down. Can I get that charge refunded?",

{"intent": [

"order_status", "refund_request", "cancel_subscription", "update_payment",

"login_problem", "shipping_delay", "bug_report", "speak_to_human", "other",

]},

)

```

Potential output:

```text

{"intent": "refund_request"}

```

### Banking request

A single customer message often mixes a failed payment, a beneficiary change, and a fee question. The label set is the product catalog: transfers, cards, fraud, mortgages. The model maps the utterance onto the operation the core system should open.

```python

model.classify_text(

"The transfer I sent this morning is still pending, and I think I used the wrong sort code. Can you stop it and add Emily as the beneficiary instead?",

{"intent": [

"transfer_pending", "transfer_cancel", "beneficiary_add", "card_lost",

"balance_inquiry", "fraud_report", "mortgage_application", "fee_explanation",

]},

)

```

Potential output:

```text

{"intent": "transfer_cancel"}

```

### Travel request

Booking, changing, cancelling, and seat requests look similar in free text and trigger different inventory calls. Use this when a chat or email has to become a structured booking action without a form.

```python

model.classify_text(

"I need to move my Friday flight to Paris to Saturday morning, same cabin, and keep the aisle seat if you can.",

{"request": ["book", "change", "cancel", "status", "seat_change", "refund", "baggage"]},

)

```

Potential output:

```text

{"request": "change"}

```

### Clinic request

Patients describe symptoms and the thing they want in the same sentence: an appointment, a refill, a result, a referral. Front-desk routing needs that distinction before anyone is put on a schedule.

```python

model.classify_text(

"The rash came back after the antibiotics finished. Can I get a same-week appointment with dermatology, or should I just refill the cream?",

{"request": [

"book_appointment", "refill_prescription", "test_results",

"referral", "billing_question", "cancel_appointment",

]},

)

```

Potential output:

```text

{"request": "book_appointment"}

```

### Review sentiment

Star ratings hide mixed reviews. A product can be praised and rejected in one paragraph. A four-way sentiment label is what a dashboard, a reply policy, or a ranking feature actually needs.

```python

model.classify_text(

"Battery dies before lunch, but the keyboard and the screen are the best I have used on a laptop.",

{"sentiment": ["positive", "negative", "mixed", "neutral"]},

)

```

Potential output:

```text

{"sentiment": "mixed"}

```

### Product aspects

Sentiment says the review is mixed. Aspects say why: battery, keyboard, screen. Several labels apply at once, so this head is multi-label. That is the input to aspect-level analytics and to a reply that mentions the right part of the product.

```python

model.classify_text(

"Battery dies before lunch, but the keyboard and the screen are the best I have used on a laptop.",

{"aspects": {

Text extracted automatically; images, tables and formatting may be missing. Original: https://huggingface.co/fastino/GLiNER2.5-Decide