diff --git a/assets/ar/sentiment_emotion_others/stance_detection/ANSStance_Jais13b_FewShot.py b/assets/ar/sentiment_emotion_others/stance_detection/ANSStance_Jais13b_FewShot.py
new file mode 100644
index 00000000..815fef8b
--- /dev/null
+++ b/assets/ar/sentiment_emotion_others/stance_detection/ANSStance_Jais13b_FewShot.py
@@ -0,0 +1,95 @@
+from llmebench.datasets import ANSStanceDataset
+from llmebench.models import FastChatModel
+from llmebench.tasks import StanceTask
+
+
+def metadata():
+    return {
+        "author": "Arabic Language Technologies, QCRI, HBKU",
+        "model": "Jais-13b",
+        "description": "Locally hosted Jais-13b-chat model using FastChat. 5-shot",
+        "scores": {"Macro-F1": "0.5272154156628485"},
+    }
+
+
+def config():
+    return {
+        "dataset": ANSStanceDataset,
+        "task": StanceTask,
+        "model": FastChatModel,
+        "model_args": {
+            "class_labels": ["agree", "disagree"],
+            "max_tries": 30,
+        },
+    }
+
+
+def prompt(input_sample, examples):
+    ref_s = input_sample["sentence_1"]
+    claim = input_sample["sentence_2"]
+    base_prompt = (
+        f"Given a reference sentence and a claim, predict whether the claim agrees or disagrees with the reference sentence. Reply only using 'agree', 'disagree', or use 'other' if the sentence and claim are unrelated."
+        f"\n\n"
+        f"reference sentence: {ref_s}"
+        f"\nclaim: {claim}"
+        f"\nlabel: \n"
+    )
+
+    return [
+        {
+            "role": "user",
+            "content": few_shot_prompt(input_sample, base_prompt, examples),
+        },
+    ]
+
+
+def few_shot_prompt(input_sample, base_prompt, examples):
+    out_prompt = base_prompt
+    for example in examples:
+        ref_s = example["input"]["sentence_1"]
+        claim = example["input"]["sentence_2"]
+
+        out_prompt = (
+            out_prompt
+            + "reference sentence: "
+            + ref_s
+            + "\nclaim: "
+            + claim
+            + "\nlabel: "
+            + example["label"]
+            + "\n\n"
+        )
+
+    # Append the sentence we want the model to predict for but leave the label blank
+
+    ref_s = input_sample["sentence_1"]
+    claim = input_sample["sentence_2"]
+
+    out_prompt = (
+        out_prompt
+        + "reference sentence: "
+        + ref_s
+        + "\nclaim: "
+        + claim
+        + "\nlabel: \n"
+    )
+
+    return out_prompt
+
+
+def post_process(response):
+    out = response["choices"][0]["message"]["content"]
+    out = out.strip().lower()
+
+    if "i apologize" in out:
+        return None
+
+    j = out.find("label:")
+    if j > 0:
+        out = out[j + len("label:") :]
+    else:
+        j = out.find(" is:\n\n")
+        if j > 0:
+            out = out[j + len(" is:\n\n") :]
+    out = out.strip().title()
+    return out.lower()
diff --git a/assets/ar/sentiment_emotion_others/stance_detection/ANSStance_JAIS13b_ZeroShot.py b/assets/ar/sentiment_emotion_others/stance_detection/ANSStance_Jais13b_ZeroShot.py
similarity index 96%
rename from assets/ar/sentiment_emotion_others/stance_detection/ANSStance_JAIS13b_ZeroShot.py
rename to assets/ar/sentiment_emotion_others/stance_detection/ANSStance_Jais13b_ZeroShot.py
index c14b22e5..89e3c00c 100644
--- a/assets/ar/sentiment_emotion_others/stance_detection/ANSStance_JAIS13b_ZeroShot.py
+++ b/assets/ar/sentiment_emotion_others/stance_detection/ANSStance_Jais13b_ZeroShot.py
@@ -8,6 +8,7 @@ def metadata():
         "author": "Arabic Language Technologies, QCRI, HBKU",
         "model": "Jais-13b-chat",
         "description": "Locally hosted Jais-13b-chat model using FastChat.",
+        "scores": {"Macro-F1": "0.465626102292769"},
     }