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--- |
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library_name: transformers |
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language: ar |
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pipeline_tag: text-classification |
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tags: |
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- text-classification |
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- intent-classification |
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- marbert |
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- egyptian-arabic |
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- nlu |
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- e-commerce |
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- customer-service |
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license: apache-2.0 |
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--- |
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# 🌍 MARBERT for Egyptian Dialect Intent Classification (syplyd-marbert-v1) |
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This is a fine-tuned version of [UBC-NLP/MARBERTv2](https://huggingface.co/UBC-NLP/MARBERTv2), specifically adapted for **intent classification** in **Egyptian Colloquial Arabic**, with a primary focus on **e-commerce** and **customer service** scenarios. |
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It enables accurate understanding of user queries in dialectal Arabic, empowering applications like chatbots, support assistants, and ticket routing systems. |
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--- |
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## 🧠 Model Details |
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- **Model Type**: `bert-for-sequence-classification` |
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- **Base Model**: [UBC-NLP/MARBERTv2](https://huggingface.co/UBC-NLP/MARBERTv2) |
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- **Language**: Arabic (Egyptian dialect) |
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- **Developer**: Shaza Aly |
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- **License**: Apache 2.0 |
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- **Repository**: [https://huggingface.co/ShazaAly/syplyd-marbert-1](https://huggingface.co/ShazaAly/syplyd-marbert-1) |
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--- |
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## 🚀 Usage |
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This model can be used directly with the Hugging Face `transformers` library: |
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```python |
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from transformers import pipeline |
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classifier = pipeline("text-classification", model="ShazaAly/syplyd-marbert-1") |
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# Example 1 |
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text_1 = "عايز أعرف الأوردر بتاعي هيوصل امتى؟" |
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print(classifier(text_1)) |
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# Output: [{'label': 'track_order_status', 'score': ...}] |
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# Example 2 |
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text_2 = "المنتج ده غالي، فيه بديل أرخص؟" |
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print(classifier(text_2)) |
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# Output: [{'label': 'product_alternatives', 'score': ...}] |
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