| | --- |
| | tags: |
| | - text-classification |
| | - intent-detection |
| | - gcc |
| | - e-commerce |
| | - agentic-commerce |
| | - ocg-dubai |
| | - gulf-retail |
| | language: |
| | - en |
| | - ar |
| | pipeline_tag: text-classification |
| | license: mit |
| | --- |
| | |
| | # GCC Intent Classifier v2 |
| |
|
| | > Built by [OCG Dubai](https://ocg-dubai.ae) — Agentic Commerce APIs for the GCC |
| |
|
| | A text classification model for detecting customer intents in GCC e-commerce conversations. Supports English and Arabic queries across common retail interaction patterns. |
| |
|
| | ## Intents |
| |
|
| | | Intent | Example | |
| | |--------|---------| |
| | | `product_search` | "Show me gold jewelry under 500 AED" | |
| | | `order_status` | "Where is my order?" | |
| | | `return_request` | "I want to return this item" | |
| | | `price_inquiry` | "How much is the Samsung S24?" | |
| | | `complaint` | "The delivery was late" | |
| | | `recommendation` | "What do you suggest for Eid gifts?" | |
| | | `store_info` | "What are your opening hours?" | |
| | | `payment_help` | "Can I pay with Tamara?" | |
| |
|
| | ## Usage |
| |
|
| | ```python |
| | from transformers import pipeline |
| | |
| | classifier = pipeline("text-classification", model="GencoDiv/intent-classifier-gcc-v2") |
| | result = classifier("I want to return the shoes I bought yesterday") |
| | # [{'label': 'return_request', 'score': 0.95}] |
| | ``` |
| |
|
| | ## Status |
| |
|
| | ⚠️ **Model weights pending upload.** This card documents the intended architecture and training plan. Model files will be uploaded after fine-tuning on GCC e-commerce conversation data. |
| |
|
| | ## Training Plan |
| |
|
| | - **Base model:** `distilbert-base-multilingual-cased` |
| | - **Fine-tuning data:** GCC e-commerce customer service logs (anonymized) |
| | - **Languages:** English + Gulf Arabic |
| | - **Target accuracy:** >90% on held-out test set |
| |
|
