Update README.md

README.md

@@ -42,10 +42,30 @@ model-index:
 This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. 
 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. 
-Normalize the text before classifying as the model uses normalized text: 
+Normalize the text before classifying as the model uses normalized text. Here's how to use the model: 
 ```python
+pip install setfit
+from setfit import SetFitModel
 from unicodedata import normalize
-query_n = normalize('NFKC', query)
+
+# Download model from Hub
+model = SetFitModel.from_pretrained("akhooli/setfit_ar_sst2")
+# Run inference
+queries = [
+        "يغلي الماء عند 100 درجة مئوية",
+        "فعلا لقد أحببت ذلك الفيلم",
+        "🤮 اﻷناناس مع البيتزا؟ إنه غير محبذ",
+    "رأيت أناسا بائسين في الطريق",
+    "لم يعجبني المطعم رغم أن السعر مقبول",  
+    "من باب جبر الخاطر هذه 3 نجوم لتقييم الخدمة",
+    "من باب جبر الخواطر، هذه نجمة واحدة لخدمة ﻻ تستحق"
+    ]
+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.