Update README.md

README.md

@@ -43,8 +43,33 @@ 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. 
-It was trained on akhooli/ar_reviews_100k_3 dataset (4500 samples, as few shot) with 68.7% accuracy.
+It was trained on akhooli/ar_reviews_100k_3 dataset (4500 samples, as few shot) with 68.7% accuracy. 
+There are 3 labels in the dataset: 0: negative, 1:positive, 2:mixed/neutral. 
+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
 
+# Download model from Hub
+model = SetFitModel.from_pretrained("akhooli/setfit_ar_100k_reviews")
+# 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 model card is auto generated.