Update README.md

README.md

@@ -39,7 +39,28 @@ This is a [SetFit](https://github.com/huggingface/setfit) model that can be used
 This SetFit model uses [akhooli/sbert_ar_nli_500k_norm](https://huggingface.co/akhooli/sbert_ar_nli_500k_norm) as the Sentence Transformer embedding model. 
 A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
 This model is trained with few shots using the [akhooli/ar_hs](https://huggingface.co/datasets/akhooli/ar_hs) dataset. The dataset uses LLM to generate labels.
+ Usage: 
+ ```python
+pip install setfit
+from setfit import SetFitModel
+from unicodedata import normalize
 
+# Download model from Hub
+model = SetFitModel.from_pretrained("akhooli/setfit_ar_hs")
+# Run inference
+queries = [
+        "سكت دهراً و نطق كفراً",
+        "الخلاف ﻻ يفسد للود قضية.",
+        "أنت شخص منبوذ. احترم أسيادك.",
+        "دع المكارم ﻻ ترحل لبغيتها واقعد فإنك أنت الطاعم الكاسي",
+    ]
+queries_n = [normalize('NFKC', query) for query in queries]
+preds = model.predict(queries_n)
+print(preds)
+# if you want to see the probabilities for each label
+probas = model.predict_proba(queries_n)
+print(probas)
+```
 The rest of this card is auto generated.
 The model has been trained using an efficient few-shot learning technique that involves: