Explain each classification use case on the model card

README.md

@@ -56,6 +56,8 @@ model = AutoExtractor.from_pretrained("fastino/GLiNER2.5-Decide")
 
 ### 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?",
@@ -68,6 +70,8 @@ model.classify_text(
 
 ### 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?",
@@ -80,6 +84,8 @@ model.classify_text(
 
 ### 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.",
@@ -89,6 +95,8 @@ model.classify_text(
 
 ### 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?",
@@ -101,6 +109,8 @@ model.classify_text(
 
 ### 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.",
@@ -110,7 +120,7 @@ model.classify_text(
 
 ### Product aspects
 
-Several labels can apply at once. Set `multi_label` and a threshold.
+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(
@@ -125,6 +135,8 @@ model.classify_text(
 
 ### News topic
 
+Wire copy, alerts, and scraped headlines have to land in a section before they are ranked or summarized. The label set is the section list of the product, not a fixed taxonomy baked into the model.
+
 ```python
 model.classify_text(
     "The central bank held rates and said inflation is still above target, pushing bank stocks lower in afternoon trading.",
@@ -134,6 +146,8 @@ model.classify_text(
 
 ### Document type
 
+Inboxes and shared drives mix invoices, contracts, resumes, and notes. Classifying the document is the gate in front of extraction: an invoice goes to payables, a contract goes to review, a resume goes to screening.
+
 ```python
 model.classify_text(
     "INVOICE 1842\nBill to: Northstar QA\nAmount due: 2,400 USD\nDue: 30 April 2026\nWire instructions are on page 2.",
@@ -143,7 +157,7 @@ model.classify_text(
 
 ### Email triage
 
-One call can score several heads on the same message.
+A shared mailbox needs three answers before a message is filed: what the sender wants, how soon it matters, and which team owns it. One call scores all three heads on the same text, so the router does not run the model three times.
 
 ```python
 model.classify_text(
@@ -158,6 +172,8 @@ model.classify_text(
 
 ### Ticket routing
 
+Employees describe a problem, not a department. Payroll, benefits, IT access, and facilities share the same portal. The queue label is the assignment, so the ticket opens in the right team instead of bouncing through a dispatcher.
+
 ```python
 model.classify_text(
     "[subject] 401k deduction missing from this paystub\n[body] Last month's contribution posted. This month the line is gone and HR told me to open a ticket.",
@@ -170,6 +186,8 @@ model.classify_text(
 
 ### Handoff to a person
 
+Most turns should stay automated. A repeated complaint, an explicit request for a human, or a case the bot cannot close should leave the flow. This is the yes/no gate in front of an agent queue.
+
 ```python
 model.classify_text(
     "This is the third time I have explained the same missing refund. Stop the bot and get me a person.",
@@ -179,6 +197,8 @@ model.classify_text(
 
 ### Did the agent finish?
 
+A trace can look busy and still be incomplete: a draft saved, a button disabled, a required field empty. Supervisors and eval harnesses need a finish decision from the goal plus the last state, not from whether the model stopped talking.
+
 ```python
 model.classify_text(
     "Goal: email the Q4 summary to every partner.\nLast action: draft saved in the hub.\nSend button is still disabled because two partners have no address.",
@@ -188,6 +208,8 @@ model.classify_text(
 
 ### Moderation
 
+User content and model output both need a policy decision before they are shown or acted on. The labels are the policy, from allow through personal data, harassment, scam, and spam, so a filter can block, redact, or escalate.
+
 ```python
 model.classify_text(
     "Post the customer's home address in the public thread so everyone can see where the package actually went.",
@@ -197,6 +219,8 @@ model.classify_text(
 
 ### Incident severity
 
+Pages and deploys produce more notes than pages. Severity is what decides whether this wakes someone up. Staging-only tag drift is not a production checkout failure, and the label should say so.
+
 ```python
 model.classify_text(
     "The deploy left resource tags inconsistent across staging. Production checkout is unaffected. No customer reports yet.",
@@ -206,7 +230,7 @@ model.classify_text(
 
 ### Urgency score
 
-Ordinal labels are plain strings.
+Some queues want a rank, not a bucket. Pass `"0"` through `"5"` as ordinary strings. A payroll cutoff before 5pm and a typo in a wiki page should not receive the same score, and the label is what the SLA system reads.
 
 ```python
 model.classify_text(
@@ -217,6 +241,8 @@ model.classify_text(
 
 ### Spam or not
 
+The first filter on an inbox or a comment stream. Phishing and mailbox-full lures should never reach the intent router. A two-label decision keeps that check cheap enough to run on every message.
+
 ```python
 model.classify_text(
     "Your mailbox is almost full. Click here in the next hour or we will delete every message.",
@@ -226,6 +252,8 @@ model.classify_text(
 
 ### Several decisions at once
 
+Real requests carry more than one fact. A guest can need a room move, a billing release, and a maintenance ticket in the same call. Intent, priority, a human gate, and multi-label topics are scored together, which is how a property system opens the right work orders without a chain of prompts.
+
 ```python
 model.classify_text(
     "Guest in room 1408 says the AC has been out since yesterday and they want to move tonight or leave. They also asked for the incidentals hold to be released.",