diff --git a/assets/ar/factuality_disinformation_harmful_content/subjectivity/CT23Subjectivity_GPT4_FewShot_Arabic.py b/assets/ar/factuality_disinformation_harmful_content/subjectivity/CT23Subjectivity_GPT4_FewShot_Arabic.py
new file mode 100755
index 00000000..8f95c058
--- /dev/null
+++ b/assets/ar/factuality_disinformation_harmful_content/subjectivity/CT23Subjectivity_GPT4_FewShot_Arabic.py
@@ -0,0 +1,64 @@
+from llmebench.datasets import CT23SubjectivityDataset
+from llmebench.models import OpenAIModel
+from llmebench.tasks import SubjectivityTask
+
+
+def metadata():
+    return {
+        "author": "Mohamed Bayan Kmainasi, Rakif Khan, Ali Ezzat Shahroor, Boushra Bendou, Maram Hasanain, and Firoj Alam",
+        "affiliation": "Arabic Language Technologies, Qatar Computing Research Institute (QCRI), Hamad Bin Khalifa University (HBKU)",
+        "model": "GPT-4o-2024-05-22",
+        "description": "For a comprehensive analysis and results, refer to our peer-reviewed publication available at [Springer](https://doi.org/10.1007/978-981-96-0576-7_30) or explore the preprint version on [arXiv](https://arxiv.org/abs/2409.07054).",
+    }
+
+
+def config():
+    return {
+        "dataset": CT23SubjectivityDataset,
+        "task": SubjectivityTask,
+        "model": OpenAIModel,
+        "model_args": {
+            "class_labels": ["SUBJ", "OBJ"],
+            "max_tries": 30,
+        },
+        "general_args": {"test_split": "ar/dev", "fewshot": {"train_split": "ar"}},
+    }
+
+
+def prompt(input_sample, examples):
+    base_prompt = 'صنف التغريدة الى "موضوعية" أو "غير موضوعية". قم بتقديم التصنيف فقط لأخر تغريدة، لا تقدم أي تبرير إضافي.\n'
+    return [
+        {
+            "role": "system",
+            "content": "أنت خبير في تحليل و تصنيف التغريدات.",
+        },
+        {
+            "role": "user",
+            "content": few_shot_prompt(input_sample, base_prompt, examples),
+        },
+    ]
+
+
+def few_shot_prompt(input_sample, base_prompt, examples):
+    out_prompt = base_prompt + "\n"
+    out_prompt += "هنا بعض الأمثلة:\n\n"
+    for index, example in enumerate(examples):
+        label = "موضوعية" if example["label"] == "OBJ" else "غير موضوعية"
+
+        out_prompt += (
+            f"مثال {index}:\n" f"التغريدة: {example['input']}\n" f"التصنيف: {label}\n\n"
+        )
+
+    out_prompt += f"التغريدة: {input_sample}\nالتصنيف: \n"
+
+    return out_prompt
+
+
+def post_process(response):
+    label = response["choices"][0]["message"]["content"].strip().lower()
+    if "غير" in label or "subj" in label or "not" in label or "ليس" in label:
+        return "SUBJ"
+    elif "موضوعية" in label or "obj" in label:
+        return "OBJ"
+    else:
+        return None
diff --git a/assets/ar/factuality_disinformation_harmful_content/subjectivity/CT23Subjectivity_GPT4_FewShot_English.py b/assets/ar/factuality_disinformation_harmful_content/subjectivity/CT23Subjectivity_GPT4_FewShot_English.py
new file mode 100755
index 00000000..bfae7aba
--- /dev/null
+++ b/assets/ar/factuality_disinformation_harmful_content/subjectivity/CT23Subjectivity_GPT4_FewShot_English.py
@@ -0,0 +1,80 @@
+from llmebench.datasets import CT23SubjectivityDataset
+from llmebench.models import OpenAIModel
+from llmebench.tasks import SubjectivityTask
+
+
+def metadata():
+    return {
+        "author": "Mohamed Bayan Kmainasi, Rakif Khan, Ali Ezzat Shahroor, Boushra Bendou, Maram Hasanain, and Firoj Alam",
+        "affiliation": "Arabic Language Technologies, Qatar Computing Research Institute (QCRI), Hamad Bin Khalifa University (HBKU)",
+        "model": "GPT-4o-2024-05-22",
+        "description": "For a comprehensive analysis and results, refer to our peer-reviewed publication available at [Springer](https://doi.org/10.1007/978-981-96-0576-7_30) or explore the preprint version on [arXiv](https://arxiv.org/abs/2409.07054).",
+    }
+
+
+def config():
+    return {
+        "dataset": CT23SubjectivityDataset,
+        "task": SubjectivityTask,
+        "model": OpenAIModel,
+        "model_args": {
+            "class_labels": ["SUBJ", "OBJ"],
+            "max_tries": 30,
+        },
+        "general_args": {"test_split": "ar/dev", "fewshot": {"train_split": "ar"}},
+    }
+
+
+def prompt(input_sample, examples):
+    base_prompt = (
+        "Classify the 'tweet' as subjective or objective. Provide only label.\n"
+    )
+    return [
+        {
+            "role": "system",
+            "content": "You are an expert in tweet classification and analysis.",
+        },
+        {
+            "role": "user",
+            "content": few_shot_prompt(input_sample, base_prompt, examples),
+        },
+    ]
+
+
+def few_shot_prompt(input_sample, base_prompt, examples):
+    out_prompt = base_prompt + "\n"
+    out_prompt = out_prompt + "Here are some examples:\n\n"
+    for index, example in enumerate(examples):
+        label = "objective" if example["label"] == "OBJ" else "subjective"
+
+        out_prompt = (
+            out_prompt
+            + "Example "
+            + str(index)
+            + ":"
+            + "\n"
+            + "tweet: "
+            + example["input"]
+            + "\nlabel: "
+            + label
+            + "\n\n"
+        )
+
+    out_prompt = out_prompt + "tweet: " + input_sample + "\nlabel: \n"
+
+    return out_prompt
+
+
+def post_process(response):
+    label = response["choices"][0]["message"]["content"].strip().lower()
+    if "obj" in label or "موضوعي" in label:
+        return "OBJ"
+    elif (
+        "subj" in label
+        or "غير" in label
+        or "لا" in label
+        or "ذاتي" in label
+        or "ليس" in label
+    ):
+        return "SUBJ"
+    return None
diff --git a/assets/ar/factuality_disinformation_harmful_content/subjectivity/CT23Subjectivity_GPT4_FewShot_Mixed.py b/assets/ar/factuality_disinformation_harmful_content/subjectivity/CT23Subjectivity_GPT4_FewShot_Mixed.py
new file mode 100755
index 00000000..33c0f5f0
--- /dev/null
+++ b/assets/ar/factuality_disinformation_harmful_content/subjectivity/CT23Subjectivity_GPT4_FewShot_Mixed.py
@@ -0,0 +1,69 @@
+from llmebench.datasets import CT23SubjectivityDataset
+from llmebench.models import OpenAIModel
+from llmebench.tasks import SubjectivityTask
+
+
+def metadata():
+    return {
+        "author": "Mohamed Bayan Kmainasi, Rakif Khan, Ali Ezzat Shahroor, Boushra Bendou, Maram Hasanain, and Firoj Alam",
+        "affiliation": "Arabic Language Technologies, Qatar Computing Research Institute (QCRI), Hamad Bin Khalifa University (HBKU)",
+        "model": "GPT-4o-2024-05-22",
+        "description": "For a comprehensive analysis and results, refer to our peer-reviewed publication available at [Springer](https://doi.org/10.1007/978-981-96-0576-7_30) or explore the preprint version on [arXiv](https://arxiv.org/abs/2409.07054).",
+    }
+
+
+def config():
+    return {
+        "dataset": CT23SubjectivityDataset,
+        "task": SubjectivityTask,
+        "model": OpenAIModel,
+        "model_args": {
+            "class_labels": ["SUBJ", "OBJ"],
+            "max_tries": 30,
+        },
+        "general_args": {"test_split": "ar/dev", "fewshot": {"train_split": "ar"}},
+    }
+
+
+def prompt(input_sample, examples):
+    base_prompt = 'صنف التغريدة الى "subjective" أو "objective". قم بتقديم التصنيف فقط لأخر تغريدة، لا تقدم أي تبرير إضافي.\n'
+    return [
+        {
+            "role": "system",
+            "content": "أنت خبير في تحليل و تصنيف التغريدات.",
+        },
+        {
+            "role": "user",
+            "content": few_shot_prompt(input_sample, base_prompt, examples),
+        },
+    ]
+
+
+def few_shot_prompt(input_sample, base_prompt, examples):
+    out_prompt = base_prompt + "\n"
+    out_prompt += "هنا بعض الأمثلة:\n\n"
+    for index, example in enumerate(examples):
+        label = "objective" if example["label"] == "OBJ" else "subjective"
+
+        out_prompt += (
+            f"مثال {index}:\n" f"التغريدة: {example['input']}\n" f"التصنيف: {label}\n\n"
+        )
+
+    out_prompt += f"التغريدة: {input_sample}\nالتصنيف: \n"
+
+    return out_prompt
+
+
+def post_process(response):
+    label = response["choices"][0]["message"]["content"].strip().lower()
+    if "obj" in label or "موضوعي" in label:
+        return "OBJ"
+    elif (
+        "subj" in label
+        or "غير" in label
+        or "لا" in label
+        or "ذاتي" in label
+        or "ليس" in label
+    ):
+        return "SUBJ"
+    return None
diff --git a/assets/ar/factuality_disinformation_harmful_content/subjectivity/CT23Subjectivity_GPT4_ZeroShot_Arabic.py b/assets/ar/factuality_disinformation_harmful_content/subjectivity/CT23Subjectivity_GPT4_ZeroShot_Arabic.py
new file mode 100755
index 00000000..7b9cc990
--- /dev/null
+++ b/assets/ar/factuality_disinformation_harmful_content/subjectivity/CT23Subjectivity_GPT4_ZeroShot_Arabic.py
@@ -0,0 +1,49 @@
+from llmebench.datasets import CT23SubjectivityDataset
+from llmebench.models import OpenAIModel
+from llmebench.tasks import SubjectivityTask
+
+
+def metadata():
+    return {
+        "author": "Mohamed Bayan Kmainasi, Rakif Khan, Ali Ezzat Shahroor, Boushra Bendou, Maram Hasanain, and Firoj Alam",
+        "affiliation": "Arabic Language Technologies, Qatar Computing Research Institute (QCRI), Hamad Bin Khalifa University (HBKU)",
+        "model": "GPT-4o-2024-05-22",
+        "description": "For a comprehensive analysis and results, refer to our peer-reviewed publication available at [Springer](https://doi.org/10.1007/978-981-96-0576-7_30) or explore the preprint version on [arXiv](https://arxiv.org/abs/2409.07054).",
+    }
+
+
+def config():
+    return {
+        "dataset": CT23SubjectivityDataset,
+        "task": SubjectivityTask,
+        "model": OpenAIModel,
+        "general_args": {"test_split": "ar/dev"},
+    }
+
+
+def prompt(input_sample):
+    prompt_string = (
+        f'صنف التغريدة الى "موضوعية" أو "غير موضوعية". قم بتقديم التصنيف فقط دون أي تبرير إضافي.\n'
+        f"التغريدة: {input_sample}\n"
+        f"التصنيف: \n"
+    )
+    return [
+        {
+            "role": "system",
+            "content": "أنت خبير في تحليل و تصنيف التغريدات.",
+        },
+        {
+            "role": "user",
+            "content": prompt_string,
+        },
+    ]
+
+
+def post_process(response):
+    label = response["choices"][0]["message"]["content"].strip().lower()
+    if "غير" in label or "subj" in label or "not" in label or "ليس" in label:
+        return "SUBJ"
+    elif "موضوعية" in label or "obj" in label:
+        return "OBJ"
+    else:
+        return None
diff --git a/assets/ar/factuality_disinformation_harmful_content/subjectivity/CT23Subjectivity_GPT4_ZeroShot_English.py b/assets/ar/factuality_disinformation_harmful_content/subjectivity/CT23Subjectivity_GPT4_ZeroShot_English.py
new file mode 100755
index 00000000..2568590a
--- /dev/null
+++ b/assets/ar/factuality_disinformation_harmful_content/subjectivity/CT23Subjectivity_GPT4_ZeroShot_English.py
@@ -0,0 +1,58 @@
+from llmebench.datasets import CT23SubjectivityDataset
+from llmebench.models import OpenAIModel
+from llmebench.tasks import SubjectivityTask
+
+
+def metadata():
+    return {
+        "author": "Mohamed Bayan Kmainasi, Rakif Khan, Ali Ezzat Shahroor, Boushra Bendou, Maram Hasanain, and Firoj Alam",
+        "affiliation": "Arabic Language Technologies, Qatar Computing Research Institute (QCRI), Hamad Bin Khalifa University (HBKU)",
+        "model": "GPT-4o-2024-05-22",
+        "description": "For a comprehensive analysis and results, refer to our peer-reviewed publication available at [Springer](https://doi.org/10.1007/978-981-96-0576-7_30) or explore the preprint version on [arXiv](https://arxiv.org/abs/2409.07054).",
+    }
+
+
+def config():
+    return {
+        "dataset": CT23SubjectivityDataset,
+        "task": SubjectivityTask,
+        "model": OpenAIModel,
+        "model_args": {
+            "class_labels": ["SUBJ", "OBJ"],
+            "max_tries": 30,
+        },
+        "general_args": {"test_split": "ar/dev"},
+    }
+
+
+def prompt(input_sample):
+    prompt_string = (
+        f'Classify the tweet as "objective" or "subjective". Provide only the label.\n\n'
+        f"tweet: {input_sample}\n"
+        f"label: \n"
+    )
+    return [
+        {
+            "role": "system",
+            "content": "You are an expert in tweet classification and analysis.",
+        },
+        {
+            "role": "user",
+            "content": prompt_string,
+        },
+    ]
+
+
+def post_process(response):
+    label = response["choices"][0]["message"]["content"].strip().lower()
+    if "obj" in label or "موضوعي" in label:
+        return "OBJ"
+    elif (
+        "subj" in label
+        or "غير" in label
+        or "لا" in label
+        or "ذاتي" in label
+        or "ليس" in label
+    ):
+        return "SUBJ"
+    return None
diff --git a/assets/ar/factuality_disinformation_harmful_content/subjectivity/CT23Subjectivity_GPT4_ZeroShot_Mixed.py b/assets/ar/factuality_disinformation_harmful_content/subjectivity/CT23Subjectivity_GPT4_ZeroShot_Mixed.py
new file mode 100755
index 00000000..56d3a6d2
--- /dev/null
+++ b/assets/ar/factuality_disinformation_harmful_content/subjectivity/CT23Subjectivity_GPT4_ZeroShot_Mixed.py
@@ -0,0 +1,54 @@
+from llmebench.datasets import CT23SubjectivityDataset
+from llmebench.models import OpenAIModel
+from llmebench.tasks import SubjectivityTask
+
+
+def metadata():
+    return {
+        "author": "Mohamed Bayan Kmainasi, Rakif Khan, Ali Ezzat Shahroor, Boushra Bendou, Maram Hasanain, and Firoj Alam",
+        "affiliation": "Arabic Language Technologies, Qatar Computing Research Institute (QCRI), Hamad Bin Khalifa University (HBKU)",
+        "model": "GPT-4o-2024-05-22",
+        "description": "For a comprehensive analysis and results, refer to our peer-reviewed publication available at [Springer](https://doi.org/10.1007/978-981-96-0576-7_30) or explore the preprint version on [arXiv](https://arxiv.org/abs/2409.07054).",
+    }
+
+
+def config():
+    return {
+        "dataset": CT23SubjectivityDataset,
+        "task": SubjectivityTask,
+        "model": OpenAIModel,
+        "general_args": {"test_split": "ar/dev"},
+    }
+
+
+def prompt(input_sample):
+    prompt_string = (
+        'صنف التغريدة الى "objective" أو "subjective". قم بتقديم التصنيف دون أي تبرير إضافي.\n'
+        f"التغريدة: {input_sample}\n"
+        f"التصنيف: \n"
+    )
+    return [
+        {
+            "role": "system",
+            "content": "أنت خبير في تحليل و تصنيف التغريدات.",
+        },
+        {
+            "role": "user",
+            "content": prompt_string,
+        },
+    ]
+
+
+def post_process(response):
+    label = response["choices"][0]["message"]["content"].strip().lower()
+    if "obj" in label or "موضوعي" in label:
+        return "OBJ"
+    elif (
+        "subj" in label
+        or "غير" in label
+        or "لا" in label
+        or "ذاتي" in label
+        or "ليس" in label
+    ):
+        return "SUBJ"
+    return None
diff --git a/assets/ar/factuality_disinformation_harmful_content/subjectivity/CT23Subjectivity_JAIS13b_FewShot_Arabic.py b/assets/ar/factuality_disinformation_harmful_content/subjectivity/CT23Subjectivity_JAIS13b_FewShot_Arabic.py
new file mode 100755
index 00000000..0d49d7e8
--- /dev/null
+++ b/assets/ar/factuality_disinformation_harmful_content/subjectivity/CT23Subjectivity_JAIS13b_FewShot_Arabic.py
@@ -0,0 +1,72 @@
+from llmebench.datasets import CT23SubjectivityDataset
+from llmebench.models import FastChatModel
+from llmebench.tasks import SubjectivityTask
+
+
+def metadata():
+    return {
+        "author": "Mohamed Bayan Kmainasi, Rakif Khan, Ali Ezzat Shahroor, Boushra Bendou, Maram Hasanain, and Firoj Alam",
+        "affiliation": "Arabic Language Technologies, Qatar Computing Research Institute (QCRI), Hamad Bin Khalifa University (HBKU)",
+        "model": "jais-13b-chat",
+        "description": "For a comprehensive analysis and results, refer to our peer-reviewed publication available at [Springer](https://doi.org/10.1007/978-981-96-0576-7_30) or explore the preprint version on [arXiv](https://arxiv.org/abs/2409.07054).",
+    }
+
+
+def config():
+    return {
+        "dataset": CT23SubjectivityDataset,
+        "task": SubjectivityTask,
+        "model": FastChatModel,
+        "model_args": {
+            "class_labels": ["SUBJ", "OBJ"],
+            "max_tries": 30,
+        },
+        "general_args": {"test_split": "ar/dev", "fewshot": {"train_split": "ar"}},
+    }
+
+
+def prompt(input_sample, examples):
+    base_prompt = ' صنف التغريدة الى "موضوعية" أو "غير موضوعية". قم بتقديم التصنيف فقط لأخر تغريدة، لا تقدم أي تبرير إضافي.:\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 + "\n"
+    out_prompt = out_prompt + "هذه بعض الأمثلة:\n\n"
+    for index, example in enumerate(examples):
+        label = "موضوعية" if example["label"] == "OBJ" else "غير موضوعية"
+
+        out_prompt = (
+            out_prompt
+            + "مثال "
+            + str(index)
+            + ":"
+            + "\n"
+            + "التغريدة: "
+            + example["input"]
+            + "\التصنيف: "
+            + label
+            + "\n\n"
+        )
+
+    out_prompt = out_prompt + "التغريدة: " + input_sample + "\التصنيف: \n"
+
+    return out_prompt
+
+
+def post_process(response):
+    label = (
+        response["choices"][0]["message"]["content"].lower().replace(".", "").strip()
+    )
+
+    if "غير" in label or "subj" in label or "not" in label or "ليس" in label:
+        return "SUBJ"
+    elif "موضوعية" in label or "obj" in label:
+        return "OBJ"
+    else:
+        return None
diff --git a/assets/ar/factuality_disinformation_harmful_content/subjectivity/CT23Subjectivity_JAIS13b_FewShot_English.py b/assets/ar/factuality_disinformation_harmful_content/subjectivity/CT23Subjectivity_JAIS13b_FewShot_English.py
new file mode 100755
index 00000000..fdd6d001
--- /dev/null
+++ b/assets/ar/factuality_disinformation_harmful_content/subjectivity/CT23Subjectivity_JAIS13b_FewShot_English.py
@@ -0,0 +1,71 @@
+from llmebench.datasets import CT23SubjectivityDataset
+from llmebench.models import FastChatModel
+from llmebench.tasks import SubjectivityTask
+
+
+def metadata():
+    return {
+        "author": "Mohamed Bayan Kmainasi, Rakif Khan, Ali Ezzat Shahroor, Boushra Bendou, Maram Hasanain, and Firoj Alam",
+        "affiliation": "Arabic Language Technologies, Qatar Computing Research Institute (QCRI), Hamad Bin Khalifa University (HBKU)",
+        "model": "jais-13b-chat",
+        "description": "For a comprehensive analysis and results, refer to our peer-reviewed publication available at [Springer](https://doi.org/10.1007/978-981-96-0576-7_30) or explore the preprint version on [arXiv](https://arxiv.org/abs/2409.07054).",
+    }
+
+
+def config():
+    return {
+        "dataset": CT23SubjectivityDataset,
+        "task": SubjectivityTask,
+        "model": FastChatModel,
+        "model_args": {
+            "class_labels": ["SUBJ", "OBJ"],
+            "max_tries": 30,
+        },
+        "general_args": {"test_split": "ar/dev", "fewshot": {"train_split": "ar"}},
+    }
+
+
+def prompt(input_sample, examples):
+    base_prompt = 'Classify the tweet as "Objective" or "Subjective". Provide the classification for the last tweet only, do not provide any additional justification:\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 + "\n"
+    out_prompt = out_prompt + "Here are some examples:\n\n"
+    for index, example in enumerate(examples):
+        label = "Objective" if example["label"] == "OBJ" else "Subjective"
+
+        out_prompt = (
+            out_prompt
+            + "Example "
+            + str(index)
+            + ":\n"
+            + "Tweet: "
+            + example["input"]
+            + "\nClassification: "
+            + label
+            + "\n\n"
+        )
+
+    out_prompt = out_prompt + "Tweet: " + input_sample + "\nClassification: \n"
+
+    return out_prompt
+
+
+def post_process(response):
+    label = (
+        response["choices"][0]["message"]["content"].lower().replace(".", "").strip()
+    )
+
+    if "غير" in label or "subj" in label or "not" in label or "ليس" in label:
+        return "SUBJ"
+    elif "موضوعية" in label or "obj" in label:
+        return "OBJ"
+    else:
+        return None
diff --git a/assets/ar/factuality_disinformation_harmful_content/subjectivity/CT23Subjectivity_JAIS13b_FewShot_Mixed.py b/assets/ar/factuality_disinformation_harmful_content/subjectivity/CT23Subjectivity_JAIS13b_FewShot_Mixed.py
new file mode 100755
index 00000000..1b0e1a01
--- /dev/null
+++ b/assets/ar/factuality_disinformation_harmful_content/subjectivity/CT23Subjectivity_JAIS13b_FewShot_Mixed.py
@@ -0,0 +1,71 @@
+from llmebench.datasets import CT23SubjectivityDataset
+from llmebench.models import FastChatModel
+from llmebench.tasks import SubjectivityTask
+
+
+def metadata():
+    return {
+        "author": "Mohamed Bayan Kmainasi, Rakif Khan, Ali Ezzat Shahroor, Boushra Bendou, Maram Hasanain, and Firoj Alam",
+        "affiliation": "Arabic Language Technologies, Qatar Computing Research Institute (QCRI), Hamad Bin Khalifa University (HBKU)",
+        "model": "jais-13b-chat",
+        "description": "For a comprehensive analysis and results, refer to our peer-reviewed publication available at [Springer](https://doi.org/10.1007/978-981-96-0576-7_30) or explore the preprint version on [arXiv](https://arxiv.org/abs/2409.07054).",
+    }
+
+
+def config():
+    return {
+        "dataset": CT23SubjectivityDataset,
+        "task": SubjectivityTask,
+        "model": FastChatModel,
+        "model_args": {
+            "class_labels": ["SUBJ", "OBJ"],
+            "max_tries": 30,
+        },
+        "general_args": {"test_split": "ar/dev", "fewshot": {"train_split": "ar"}},
+    }
+
+
+def prompt(input_sample, examples):
+    base_prompt = "صنف الجملة على أنها subjective أو objective. قدم فقط التسمية.\n\nالنص: {input_sample}\التصنيف: "
+    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 + "\n"
+    out_prompt = out_prompt + "هذه بعض الأمثلة:\n\n"
+    for index, example in enumerate(examples):
+        label = "objective" if example["label"] == "OBJ" else "subjective"
+
+        out_prompt = (
+            out_prompt
+            + "مثال "
+            + str(index)
+            + ":"
+            + "\n"
+            + "التغريدة: "
+            + example["input"]
+            + "\التصنيف: "
+            + label
+            + "\n\n"
+        )
+
+    out_prompt = out_prompt + "التغريدة: " + input_sample + "\التصنيف: \n"
+
+    return out_prompt
+
+
+def post_process(response):
+    label = (
+        response["choices"][0]["message"]["content"].lower().replace(".", "").strip()
+    )
+    print(label)
+    label = label.lower()
+    if "subjective" in label or "sub" in label:
+        return "SUBJ"
+    return "OBJ"
+    return label
diff --git a/assets/ar/factuality_disinformation_harmful_content/subjectivity/CT23Subjectivity_JAIS13b_ZeroShot_English.py b/assets/ar/factuality_disinformation_harmful_content/subjectivity/CT23Subjectivity_JAIS13b_ZeroShot_English.py
new file mode 100755
index 00000000..206cae81
--- /dev/null
+++ b/assets/ar/factuality_disinformation_harmful_content/subjectivity/CT23Subjectivity_JAIS13b_ZeroShot_English.py
@@ -0,0 +1,49 @@
+from llmebench.datasets import CT23SubjectivityDataset
+from llmebench.models import FastChatModel
+from llmebench.tasks import SubjectivityTask
+
+
+def metadata():
+    return {
+        "author": "Mohamed Bayan Kmainasi, Rakif Khan, Ali Ezzat Shahroor, Boushra Bendou, Maram Hasanain, and Firoj Alam",
+        "affiliation": "Arabic Language Technologies, Qatar Computing Research Institute (QCRI), Hamad Bin Khalifa University (HBKU)",
+        "model": "jais-13b-chat",
+        "description": "For a comprehensive analysis and results, refer to our peer-reviewed publication available at [Springer](https://doi.org/10.1007/978-981-96-0576-7_30) or explore the preprint version on [arXiv](https://arxiv.org/abs/2409.07054).",
+    }
+
+
+def config():
+    return {
+        "dataset": CT23SubjectivityDataset,
+        "task": SubjectivityTask,
+        "model": FastChatModel,
+        "general_args": {"test_split": "ar/dev"},
+    }
+
+
+def prompt(input_sample):
+    return [
+        {
+            "role": "user",
+            "content": (
+                'Classify the tweet as "Objective" or "Subjective". Provide only the classification.\n\n'
+                + "Tweet: "
+                + input_sample
+                + "Classification: "
+            ),
+        }
+    ]
+
+
+def post_process(response):
+    label = (
+        response["choices"][0]["message"]["content"].lower().replace(".", "").strip()
+    )
+    # print(label)
+
+    if "غير" in label or "subj" in label or "not" in label or "ليس" in label:
+        return "SUBJ"
+    elif "موضوعية" in label or "obj" in label:
+        return "OBJ"
+    else:
+        return None
diff --git a/assets/ar/factuality_disinformation_harmful_content/subjectivity/CT23Subjectivity_JAIS13b_ZeroShot_Mixed.py b/assets/ar/factuality_disinformation_harmful_content/subjectivity/CT23Subjectivity_JAIS13b_ZeroShot_Mixed.py
new file mode 100755
index 00000000..1a3ea668
--- /dev/null
+++ b/assets/ar/factuality_disinformation_harmful_content/subjectivity/CT23Subjectivity_JAIS13b_ZeroShot_Mixed.py
@@ -0,0 +1,42 @@
+from llmebench.datasets import CT23SubjectivityDataset
+from llmebench.models import FastChatModel
+from llmebench.tasks import SubjectivityTask
+
+
+def metadata():
+    return {
+        "author": "Mohamed Bayan Kmainasi, Rakif Khan, Ali Ezzat Shahroor, Boushra Bendou, Maram Hasanain, and Firoj Alam",
+        "affiliation": "Arabic Language Technologies, Qatar Computing Research Institute (QCRI), Hamad Bin Khalifa University (HBKU)",
+        "model": "jais-13b-chat",
+        "description": "For a comprehensive analysis and results, refer to our peer-reviewed publication available at [Springer](https://doi.org/10.1007/978-981-96-0576-7_30) or explore the preprint version on [arXiv](https://arxiv.org/abs/2409.07054).",
+    }
+
+
+def config():
+    return {
+        "dataset": CT23SubjectivityDataset,
+        "task": SubjectivityTask,
+        "model": FastChatModel,
+        "general_args": {"test_split": "ar/dev"},
+    }
+
+
+def prompt(input_sample):
+    base_prompt = f"صنف الجملة على أنها subjective أو objective. قدم فقط التسمية.\n\nالنص: {input_sample}\nالتصنيف: "
+
+    return [
+        {
+            "role": "user",
+            "content": base_prompt,
+        },
+    ]
+
+
+def post_process(response):
+    label = (
+        response["choices"][0]["message"]["content"].lower().replace(".", "").strip()
+    )
+
+    if "subjective" in label or "subj" "ليس" in label or "غير" in label:
+        return "SUBJ"
+    return "OBJ"
diff --git a/assets/ar/factuality_disinformation_harmful_content/subjectivity/CT23Subjectivity_Jais13b_ZeroShot_Arabic.py b/assets/ar/factuality_disinformation_harmful_content/subjectivity/CT23Subjectivity_Jais13b_ZeroShot_Arabic.py
new file mode 100755
index 00000000..8760822d
--- /dev/null
+++ b/assets/ar/factuality_disinformation_harmful_content/subjectivity/CT23Subjectivity_Jais13b_ZeroShot_Arabic.py
@@ -0,0 +1,44 @@
+from llmebench.datasets import CT23SubjectivityDataset
+from llmebench.models import FastChatModel
+from llmebench.tasks import SubjectivityTask
+
+
+def metadata():
+    return {
+        "author": "Mohamed Bayan Kmainasi, Rakif Khan, Ali Ezzat Shahroor, Boushra Bendou, Maram Hasanain, and Firoj Alam",
+        "affiliation": "Arabic Language Technologies, Qatar Computing Research Institute (QCRI), Hamad Bin Khalifa University (HBKU)",
+        "model": "jais-13b-chat",
+        "description": "For a comprehensive analysis and results, refer to our peer-reviewed publication available at [Springer](https://doi.org/10.1007/978-981-96-0576-7_30) or explore the preprint version on [arXiv](https://arxiv.org/abs/2409.07054).",
+    }
+
+
+def config():
+    return {
+        "dataset": CT23SubjectivityDataset,
+        "task": SubjectivityTask,
+        "model": FastChatModel,
+        "general_args": {"test_split": "ar/dev"},
+    }
+
+
+def prompt(input_sample):
+    base_prompt = f'صنف التغريدة الى "موضوعية" أو "غير موضوعية". قم بتقديم التصنيف فقط.\n\nالتغريدة: {input_sample}\nالتصنيف: '
+    return [
+        {
+            "role": "user",
+            "content": base_prompt,
+        },
+    ]
+
+
+def post_process(response):
+    label = (
+        response["choices"][0]["message"]["content"].lower().replace(".", "").strip()
+    )
+
+    if "غير" in label or "subj" in label or "not" in label or "ليس" in label:
+        return "SUBJ"
+    elif "موضوعية" in label or "obj" in label:
+        return "OBJ"
+    else:
+        return None
diff --git a/assets/ar/factuality_disinformation_harmful_content/subjectivity/CT23Subjectivity_LLama3-8b_FewShot_Arabic.py b/assets/ar/factuality_disinformation_harmful_content/subjectivity/CT23Subjectivity_LLama3-8b_FewShot_Arabic.py
new file mode 100755
index 00000000..452ea072
--- /dev/null
+++ b/assets/ar/factuality_disinformation_harmful_content/subjectivity/CT23Subjectivity_LLama3-8b_FewShot_Arabic.py
@@ -0,0 +1,78 @@
+from llmebench.datasets import CT23SubjectivityDataset
+from llmebench.models import AzureModel
+from llmebench.tasks import SubjectivityTask
+
+
+def metadata():
+    return {
+        "author": "Mohamed Bayan Kmainasi, Rakif Khan, Ali Ezzat Shahroor, Boushra Bendou, Maram Hasanain, and Firoj Alam",
+        "affiliation": "Arabic Language Technologies, Qatar Computing Research Institute (QCRI), Hamad Bin Khalifa University (HBKU)",
+        "model": "Llama-3.1-8B-Instruct",
+        "description": "For a comprehensive analysis and results, refer to our peer-reviewed publication available at [Springer](https://doi.org/10.1007/978-981-96-0576-7_30) or explore the preprint version on [arXiv](https://arxiv.org/abs/2409.07054).",
+    }
+
+
+def config():
+    return {
+        "dataset": CT23SubjectivityDataset,
+        "task": SubjectivityTask,
+        "model": AzureModel,
+        "model_args": {
+            "class_labels": ["SUBJ", "OBJ"],
+            "max_tries": 30,
+        },
+        "general_args": {"test_split": "ar/dev", "fewshot": {"train_split": "ar"}},
+    }
+
+
+def prompt(input_sample, examples):
+    base_prompt = ' صنف التغريدة الى "موضوعية" أو "غير موضوعية". قم بتقديم التصنيف فقط لأخر تغريدة، لا تقدم أي تبرير إضافي.:\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 + "\n"
+    out_prompt = out_prompt + "هذه بعض الأمثلة:\n\n"
+    for index, example in enumerate(examples):
+        label = "موضوعية" if example["label"] == "OBJ" else "غير موضوعية"
+
+        out_prompt = (
+            out_prompt
+            + "مثال "
+            + str(index)
+            + ":"
+            + "\n"
+            + "التغريدة: "
+            + example["input"]
+            + "\التصنيف: "
+            + label
+            + "\n\n"
+        )
+
+    out_prompt = out_prompt + "التغريدة: " + input_sample + "\التصنيف: \n"
+
+    return out_prompt
+
+
+def post_process(response):
+    # print(response)
+    if "output" in response:
+        # if "content" in response["messages"]:
+        label = response["output"].strip()
+        label = label.replace("<s>", "")
+        label = label.replace("</s>", "")
+        label = label.lower()
+    else:
+        print("Response .. " + str(response))
+        return None
+    if "غير" in label or "subj" in label or "not" in label or "ليس" in label:
+        return "SUBJ"
+    elif "موضوعية" in label or "obj" in label:
+        return "OBJ"
+    else:
+        return None
diff --git a/assets/ar/factuality_disinformation_harmful_content/subjectivity/CT23Subjectivity_LLama3-8b_FewShot_English.py b/assets/ar/factuality_disinformation_harmful_content/subjectivity/CT23Subjectivity_LLama3-8b_FewShot_English.py
new file mode 100755
index 00000000..e9d2778c
--- /dev/null
+++ b/assets/ar/factuality_disinformation_harmful_content/subjectivity/CT23Subjectivity_LLama3-8b_FewShot_English.py
@@ -0,0 +1,77 @@
+from llmebench.datasets import CT23SubjectivityDataset
+from llmebench.models import AzureModel
+from llmebench.tasks import SubjectivityTask
+
+
+def metadata():
+    return {
+        "author": "Mohamed Bayan Kmainasi, Rakif Khan, Ali Ezzat Shahroor, Boushra Bendou, Maram Hasanain, and Firoj Alam",
+        "affiliation": "Arabic Language Technologies, Qatar Computing Research Institute (QCRI), Hamad Bin Khalifa University (HBKU)",
+        "model": "Llama-3.1-8B-Instruct",
+        "description": "For a comprehensive analysis and results, refer to our peer-reviewed publication available at [Springer](https://doi.org/10.1007/978-981-96-0576-7_30) or explore the preprint version on [arXiv](https://arxiv.org/abs/2409.07054).",
+    }
+
+
+def config():
+    return {
+        "dataset": CT23SubjectivityDataset,
+        "task": SubjectivityTask,
+        "model": AzureModel,
+        "model_args": {
+            "class_labels": ["SUBJ", "OBJ"],
+            "max_tries": 30,
+        },
+        "general_args": {"test_split": "ar/dev", "fewshot": {"train_split": "ar"}},
+    }
+
+
+def prompt(input_sample, examples):
+    base_prompt = 'Classify the tweet as "Objective" or "Subjective". Provide the classification for the last tweet only, do not provide any additional justification:\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 + "\n"
+    out_prompt = out_prompt + "Here are some examples:\n\n"
+    for index, example in enumerate(examples):
+        label = "Objective" if example["label"] == "OBJ" else "Subjective"
+
+        out_prompt = (
+            out_prompt
+            + "Example "
+            + str(index)
+            + ":\n"
+            + "Tweet: "
+            + example["input"]
+            + "\nClassification: "
+            + label
+            + "\n\n"
+        )
+
+    out_prompt = out_prompt + "Tweet: " + input_sample + "\nClassification: \n"
+
+    return out_prompt
+
+
+def post_process(response):
+    # print(response)
+    if "output" in response:
+        # if "content" in response["messages"]:
+        label = response["output"].strip()
+        label = label.replace("<s>", "")
+        label = label.replace("</s>", "")
+        label = label.lower()
+    else:
+        print("Response .. " + str(response))
+        return None
+    if "غير" in label or "subj" in label or "not" in label or "ليس" in label:
+        return "SUBJ"
+    elif "موضوعية" in label or "obj" in label:
+        return "OBJ"
+    else:
+        return None
diff --git a/assets/ar/factuality_disinformation_harmful_content/subjectivity/CT23Subjectivity_LLama3-8b_FewShot_Mixed.py b/assets/ar/factuality_disinformation_harmful_content/subjectivity/CT23Subjectivity_LLama3-8b_FewShot_Mixed.py
new file mode 100755
index 00000000..db6da233
--- /dev/null
+++ b/assets/ar/factuality_disinformation_harmful_content/subjectivity/CT23Subjectivity_LLama3-8b_FewShot_Mixed.py
@@ -0,0 +1,78 @@
+from llmebench.datasets import CT23SubjectivityDataset
+from llmebench.models import AzureModel
+from llmebench.tasks import SubjectivityTask
+
+
+def metadata():
+    return {
+        "author": "Mohamed Bayan Kmainasi, Rakif Khan, Ali Ezzat Shahroor, Boushra Bendou, Maram Hasanain, and Firoj Alam",
+        "affiliation": "Arabic Language Technologies, Qatar Computing Research Institute (QCRI), Hamad Bin Khalifa University (HBKU)",
+        "model": "Llama-3.1-8B-Instruct",
+        "description": "For a comprehensive analysis and results, refer to our peer-reviewed publication available at [Springer](https://doi.org/10.1007/978-981-96-0576-7_30) or explore the preprint version on [arXiv](https://arxiv.org/abs/2409.07054).",
+    }
+
+
+def config():
+    return {
+        "dataset": CT23SubjectivityDataset,
+        "task": SubjectivityTask,
+        "model": AzureModel,
+        "model_args": {
+            "class_labels": ["SUBJ", "OBJ"],
+            "max_tries": 30,
+        },
+        "general_args": {"test_split": "ar/dev", "fewshot": {"train_split": "ar"}},
+    }
+
+
+def prompt(input_sample, examples):
+    base_prompt = 'صنف التغريدة الى "subjective" أو "objective". قم بتقديم التصنيف فقط.'
+    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 + "\n"
+    out_prompt = out_prompt + "هذه بعض الأمثلة:\n\n"
+    for index, example in enumerate(examples):
+        label = "objective" if example["label"] == "OBJ" else "subjective"
+
+        out_prompt = (
+            out_prompt
+            + "مثال "
+            + str(index)
+            + ":"
+            + "\n"
+            + "التغريدة: "
+            + example["input"]
+            + "التصنيف: "
+            + label
+            + "\n\n"
+        )
+
+    out_prompt = out_prompt + "التغريدة: " + input_sample + "التصنيف: \n"
+
+    return out_prompt
+
+
+def post_process(response):
+    # print(response)
+    if "output" in response:
+        # if "content" in response["messages"]:
+        label = response["output"].strip()
+        label = label.replace("<s>", "")
+        label = label.replace("</s>", "")
+        label = label.lower()
+    else:
+        print("Response .. " + str(response))
+        return None
+    if "غير" in label or "subj" in label or "not" in label or "ليس" in label:
+        return "SUBJ"
+    elif "موضوعية" in label or "obj" in label:
+        return "OBJ"
+    else:
+        return None
diff --git a/assets/ar/factuality_disinformation_harmful_content/subjectivity/CT23Subjectivity_LLama3-8b_ZeroShot_Arabic.py b/assets/ar/factuality_disinformation_harmful_content/subjectivity/CT23Subjectivity_LLama3-8b_ZeroShot_Arabic.py
new file mode 100755
index 00000000..cca523c3
--- /dev/null
+++ b/assets/ar/factuality_disinformation_harmful_content/subjectivity/CT23Subjectivity_LLama3-8b_ZeroShot_Arabic.py
@@ -0,0 +1,58 @@
+from llmebench.datasets import CT23SubjectivityDataset
+from llmebench.models import AzureModel
+from llmebench.tasks import SubjectivityTask
+
+
+def metadata():
+    return {
+        "author": "Mohamed Bayan Kmainasi, Rakif Khan, Ali Ezzat Shahroor, Boushra Bendou, Maram Hasanain, and Firoj Alam",
+        "affiliation": "Arabic Language Technologies, Qatar Computing Research Institute (QCRI), Hamad Bin Khalifa University (HBKU)",
+        "model": "Llama-3.1-8B-Instruct",
+        "description": "For a comprehensive analysis and results, refer to our peer-reviewed publication available at [Springer](https://doi.org/10.1007/978-981-96-0576-7_30) or explore the preprint version on [arXiv](https://arxiv.org/abs/2409.07054).",
+    }
+
+
+def config():
+    return {
+        "dataset": CT23SubjectivityDataset,
+        "task": SubjectivityTask,
+        "model": AzureModel,
+        "model_args": {
+            "class_labels": ["SUBJ", "OBJ"],
+            "max_tries": 3,
+        },
+        "general_args": {"test_split": "ar/dev"},
+    }
+
+
+def prompt(input_sample):
+    return [
+        {
+            "role": "user",
+            "content": (
+                ' صنف التغريدة الى "موضوعية" أو "غير موضوعية". قم بتقديم التصنيف فقط.\n\n'
+                + "التغريدة: "
+                + input_sample
+                + "التصنيف: "
+            ),
+        }
+    ]
+
+
+def post_process(response):
+    # print(response)
+    if "output" in response:
+        # if "content" in response["messages"]:
+        label = response["output"].strip()
+        label = label.replace("<s>", "")
+        label = label.replace("</s>", "")
+        label = label.lower()
+    else:
+        print("Response .. " + str(response))
+        return None
+    if "غير" in label or "subj" in label or "not" in label or "ليس" in label:
+        return "SUBJ"
+    elif "موضوعية" in label or "obj" in label:
+        return "OBJ"
+    else:
+        return None
diff --git a/assets/ar/factuality_disinformation_harmful_content/subjectivity/CT23Subjectivity_LLama3-8b_ZeroShot_English.py b/assets/ar/factuality_disinformation_harmful_content/subjectivity/CT23Subjectivity_LLama3-8b_ZeroShot_English.py
new file mode 100755
index 00000000..ada805a4
--- /dev/null
+++ b/assets/ar/factuality_disinformation_harmful_content/subjectivity/CT23Subjectivity_LLama3-8b_ZeroShot_English.py
@@ -0,0 +1,58 @@
+from llmebench.datasets import CT23SubjectivityDataset
+from llmebench.models import AzureModel
+from llmebench.tasks import SubjectivityTask
+
+
+def metadata():
+    return {
+        "author": "Mohamed Bayan Kmainasi, Rakif Khan, Ali Ezzat Shahroor, Boushra Bendou, Maram Hasanain, and Firoj Alam",
+        "affiliation": "Arabic Language Technologies, Qatar Computing Research Institute (QCRI), Hamad Bin Khalifa University (HBKU)",
+        "model": "Llama-3.1-8B-Instruct",
+        "description": "For a comprehensive analysis and results, refer to our peer-reviewed publication available at [Springer](https://doi.org/10.1007/978-981-96-0576-7_30) or explore the preprint version on [arXiv](https://arxiv.org/abs/2409.07054).",
+    }
+
+
+def config():
+    return {
+        "dataset": CT23SubjectivityDataset,
+        "task": SubjectivityTask,
+        "model": AzureModel,
+        "model_args": {
+            "class_labels": ["SUBJ", "OBJ"],
+            "max_tries": 3,
+        },
+        "general_args": {"test_split": "ar/dev"},
+    }
+
+
+def prompt(input_sample):
+    return [
+        {
+            "role": "user",
+            "content": (
+                'Classify the tweet as "Objective" or "Subjective". Provide only the classification.\n\n'
+                + "Tweet: "
+                + input_sample
+                + "Classification: "
+            ),
+        }
+    ]
+
+
+def post_process(response):
+    # print(response)
+    if "output" in response:
+        # if "content" in response["messages"]:
+        label = response["output"].strip()
+        label = label.replace("<s>", "")
+        label = label.replace("</s>", "")
+        label = label.lower()
+    else:
+        print("Response .. " + str(response))
+        return None
+    if "غير" in label or "subj" in label or "not" in label or "ليس" in label:
+        return "SUBJ"
+    elif "موضوعية" in label or "obj" in label:
+        return "OBJ"
+    else:
+        return None
diff --git a/assets/ar/factuality_disinformation_harmful_content/subjectivity/CT23Subjectivity_LLama3-8b_ZeroShot_Mixed.py b/assets/ar/factuality_disinformation_harmful_content/subjectivity/CT23Subjectivity_LLama3-8b_ZeroShot_Mixed.py
new file mode 100755
index 00000000..3ded318b
--- /dev/null
+++ b/assets/ar/factuality_disinformation_harmful_content/subjectivity/CT23Subjectivity_LLama3-8b_ZeroShot_Mixed.py
@@ -0,0 +1,58 @@
+from llmebench.datasets import CT23SubjectivityDataset
+from llmebench.models import AzureModel
+from llmebench.tasks import SubjectivityTask
+
+
+def metadata():
+    return {
+        "author": "Mohamed Bayan Kmainasi, Rakif Khan, Ali Ezzat Shahroor, Boushra Bendou, Maram Hasanain, and Firoj Alam",
+        "affiliation": "Arabic Language Technologies, Qatar Computing Research Institute (QCRI), Hamad Bin Khalifa University (HBKU)",
+        "model": "Llama-3.1-8B-Instruct",
+        "description": "For a comprehensive analysis and results, refer to our peer-reviewed publication available at [Springer](https://doi.org/10.1007/978-981-96-0576-7_30) or explore the preprint version on [arXiv](https://arxiv.org/abs/2409.07054).",
+    }
+
+
+def config():
+    return {
+        "dataset": CT23SubjectivityDataset,
+        "task": SubjectivityTask,
+        "model": AzureModel,
+        "model_args": {
+            "class_labels": ["SUBJ", "OBJ"],
+            "max_tries": 3,
+        },
+        "general_args": {"test_split": "ar/dev"},
+    }
+
+
+def prompt(input_sample):
+    return [
+        {
+            "role": "user",
+            "content": (
+                'صنف الجملة الى "subjective" أو "objective". قم بتقديم التصنيف فقط.\n\n'
+                + "التغريدة: "
+                + input_sample
+                + "\nالتصنيف: "
+            ),
+        }
+    ]
+
+
+def post_process(response):
+    # print(response)
+    if "output" in response:
+        # if "content" in response["messages"]:
+        label = response["output"].strip()
+        label = label.replace("<s>", "")
+        label = label.replace("</s>", "")
+        label = label.lower()
+    else:
+        print("Response .. " + str(response))
+        return None
+    if "غير" in label or "subj" in label or "not" in label or "ليس" in label:
+        return "SUBJ"
+    elif "موضوعية" in label or "obj" in label:
+        return "OBJ"
+    else:
+        return None