Text Classification
Transformers
Safetensors
Arabic
bert
arabic
nlp
saudi
dialectal-arabic
complaints
restaurant-reviews
aspect-classification
ambience-detection
Eval Results (legacy)
text-embeddings-inference
Instructions to use FerasMad/arabic-complaints-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FerasMad/arabic-complaints-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="FerasMad/arabic-complaints-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("FerasMad/arabic-complaints-classifier") model = AutoModelForSequenceClassification.from_pretrained("FerasMad/arabic-complaints-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download label_map.json from FerasMad/arabic-complaints-classifier: direct link, hf CLI and curl.
- Browser
- Download file 230 Bytes
-
https://huggingface.co/FerasMad/arabic-complaints-classifier/resolve/main/label_map.json
- Command line
-
hf download hf://FerasMad/arabic-complaints-classifier/label_map.json
-
curl -L -o label_map.json https://huggingface.co/FerasMad/arabic-complaints-classifier/resolve/main/label_map.json
230 Bytes
| { | |
| "التوصيل": 0, | |
| "السعر والقيمة": 1, | |
| "النظافة": 2, | |
| "جودة الطعام": 3, | |
| "خدمة الموظفين": 4, | |
| "دقة الطلب": 5, | |
| "عامة": 6, | |
| "وقت الانتظار": 7 | |
| } |