update

README.md

@@ -1,14 +1,12 @@
 ---
-title: Mizan Leaderboard
-emoji: 🔥
-colorFrom: green
+title: Leaderboard
+emoji: 🐨
+colorFrom: red
 colorTo: gray
 sdk: gradio
-sdk_version: 5.31.0
+sdk_version: 5.21.0
 app_file: app.py
 pinned: false
-license: cc-by-nc-nd-4.0
-short_description: 'MIZAN: A Persian LLM Leaderboard'
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

about.py

@@ -0,0 +1,91 @@
+# in about.py
+
+import gradio as gr
+
+def render_about():
+    with gr.Blocks() as about_page:
+        gr.Markdown("""
+        # About MIZAN: A Persian LLM Leaderboard
+
+        MIZAN: A Persian LLM Leaderboard is designed to establish a standard and comprehensive benchmark for evaluating Large Language Models (LLMs) in the Persian language. This project combines existing datasets, translates and localizes globally recognized benchmarks, and incorporates newly developed, Persian-specific datasets. MIZAN aims to provide a multi-dimensional assessment of models' capabilities across various linguistic, knowledge-based, and reasoning tasks. Its primary goal is to offer researchers, developers, and enthusiasts a transparent and reliable view of LLM performance in the Persian language landscape.
+
+        MIZAN provides a holistic view of models' strengths and weaknesses by assessing them across a suite of key tasks, contributing to the advancement of AI research for the Persian language.
+        """)
+
+        with gr.Accordion("MIZAN Benchmark Components Details", open=True): # Changed from PULL
+            
+            with gr.Accordion("1. PerCoR (Persian Commonsense Reasoning)", open=False):
+                gr.Markdown("""
+                PersCoR is the first large-scale Persian benchmark for evaluating models' ability in **commonsense reasoning** through multi-choice sentence completion. It includes over 106,000 samples from diverse domains such as news, religion, and lifestyle, extracted from more than 40 Persian websites. Innovative methods like "segmentation by conjunctions" were used to create coherent and diverse sentences and options, while the DRESS-AF technique helped generate challenging, human-solvable distractors.
+                """)
+
+            with gr.Accordion("2. IFEval-fa (Persian Instruction Following Evaluation)", open=False):
+                gr.Markdown("""
+                This dataset is a Persian-adapted and localized version of **IFEval**, assessing models' proficiency in **accurately executing complex, multi-step instructions (Instruction Following)**. The translation process involved a hybrid machine-human approach, with prompts unsuitable for the Persian language being rewritten or removed.
+                """)
+
+            with gr.Accordion("3. MMLU-Fa (Persian Massive Multitask Language Understanding)", open=False):
+                gr.Markdown("""
+                MMLU-Fa is an expanded and localized version of the renowned **MMLU** benchmark, designed to measure **general and specialized knowledge** of models in Persian. Tailored to cover knowledge at various levels and relevant to the Iranian cultural context, it comprises three main sub-datasets:
+                <ul>
+                    <li><strong>SPK (School Persian Knowledge):</strong> Contains 5,581 multiple-choice questions from the official Iranian school curriculum (grades 4-12) across 78 diverse subjects. Data was collected from the "Paadars" educational website and subsequently cleaned.</li>
+                    <li><strong>UPK (University Persian Knowledge):</strong> Includes 7,793 multiple-choice questions from Master's and PhD entrance exams across 25 academic disciplines (e.g., medicine, engineering, humanities, arts). This data was extracted from exam booklets using OCR technology and cleaned by LLMs.</li>
+                    <li><strong>GPK (General Persian Knowledge):</strong> Consists of 1,003 multiple-choice questions on 15 topics related to general knowledge specific to Iranian society (e.g., city souvenirs, religious edicts, national laws, famous personalities, cultural idioms). This data was generated using LLMs with specific prompts and reviewed by humans.</li>
+                </ul>
+                """)
+
+            with gr.Accordion("4. Persian MT-Bench (Persian Multi-Turn Benchmark)", open=False):
+                gr.Markdown("""
+                This is a localized version of the **MT-Bench** benchmark, evaluating models on **multi-turn question-answering and dialogue-based tasks**. Questions involve multi-step requests or require creative responses. In the Persian version, all samples were translated and rewritten by humans, and some were expanded to 3 or 4 turns. Two new topics were also added:
+                <ul>
+                    <li><strong>Native Iranian Knowledge:</strong> Questions about cultural topics such as films, actors, and Iranian figures.</li>
+                    <li><strong>Chat-Retrieval:</strong> Involves a multi-turn dialogue where the model must extract a relevant question and answer based on the user's needs.</li>
+                </ul>
+                """)
+
+            with gr.Accordion("5. Persian NLU (Persian Natural Language Understanding)", open=False):
+                gr.Markdown("""
+                This section comprises a collection of existing Persian benchmarks for evaluating various aspects of **Natural Language Understanding**. Key tasks and datasets include:
+                <ul>
+                    <li><strong>Sentiment Analysis:</strong> DeepSentiPers</li>
+                    <li><strong>Text Classification:</strong> Synthetic Persian Tone, SID</li>
+                    <li><strong>Natural Language Inference (NLI):</strong> FarsTAIL</li>
+                    <li><strong>Semantic Textual Similarity (STS):</strong> Synthetic Persian STS, FarSICK</li>
+                    <li><strong>Named Entity Recognition (NER):</strong> Arman</li>
+                    <li><strong>Paraphrase Detection:</strong> FarsiParaphraseDetection, ParsiNLU</li>
+                    <li><strong>Extractive Question Answering (EQA):</strong> PQuAD</li>
+                    <li><strong>Keyword Extraction:</strong> Synthetic Persian Keywords</li>
+                </ul>
+                """)
+
+            with gr.Accordion("6. Persian NLG (Persian Natural Language Generation)", open=False):
+                gr.Markdown("""
+                This section focuses on **Natural Language Generation**, covering tasks such as:
+                <ul>
+                    <li><strong>Summarization:</strong> SamSUM-fa, PnSummary</li>
+                    <li><strong>Machine Translation:</strong> TEP, MIZAN, EPOQUE</li>
+                    <li><strong>Question Generation:</strong> PersianQA</li>
+                </ul>
+                The goal is to assess the generative capabilities of models.
+                """)
+
+            # with gr.Accordion("7. BoolQ-fa (Persian Boolean Question Answering)", open=False):
+            #     gr.Markdown("""
+            #     A Persian-adapted version of **BoolQ**, this benchmark evaluates a model's ability to answer **yes/no questions based on a given text**, testing common reasoning skills. Each instance includes a passage, a question about it, and a boolean answer.
+            #     """)
+
+            # with gr.Accordion("8. PIQA-fa (Persian Physical Interaction Question Answering)", open=False):
+            #     gr.Markdown("""
+            #     This is a Persian version of **PIQA**, focusing on **physical reasoning and commonsense understanding** of real-world interactions. Each instance presents a goal or question along with two potential solutions, requiring the model to choose the more physically plausible option.
+            #     """)
+
+        gr.Markdown("""
+        ---
+        MIZAN is a significant step towards the scientific and localized evaluation of language models for Persian, aiming to serve as a valuable assessment reference for researchers, developers, and anyone interested in practical language models.
+        """)
+
+    return about_page
+
+# To test this function directly (if in a separate file):
+# if __name__ == '__main__':
+#     render_about().launch()

app.py

@@ -0,0 +1,87 @@
+import gradio as gr
+from pathlib import Path
+import logging
+
+# Import LeaderboardApp from the correct location within the 'leaderboard' package
+from leaderboard.leaderboard import LeaderboardApp
+
+# Import UI rendering functions for other tabs
+from about import render_about
+from submission import render_submit
+
+# --- Logging Setup (Optional but Recommended) ---
+# You can centralize logging configuration here or ensure each module handles its own.
+# For simplicity, if other modules already configure logging, this might not be strictly needed here.
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s - %(levelname)s - %(module)s - %(message)s"
+)
+logger = logging.getLogger(__name__)
+
+def create_app():
+    """
+    Creates and configures the main Gradio application for MIZAN: A Persian LLM Leaderboard.
+    """
+    logger.info("Initializing MIZAN: A Persian LLM Leaderboard application...")
+
+    # Define the path to the leaderboard's configuration file
+    # This assumes app.py is in the project root, and column_config.yaml is inside the 'leaderboard' directory.
+    config_file_path = Path("leaderboard/leaderboard_config.yaml")
+
+    if not config_file_path.exists():
+        logger.error(f"CRITICAL: Leaderboard configuration file not found at {config_file_path}. The application may not function correctly.")
+        # Optionally, you could raise an error here or return a Gradio interface indicating the error.
+
+    # Initialize the LeaderboardApp with the configuration path
+    leaderboard_processor = LeaderboardApp(config_path=config_file_path)
+
+    # Load and process data for the leaderboard
+    logger.info("Loading and processing leaderboard data...")
+    leaderboard_processor.load_data()
+    leaderboard_processor.handle_nulls_in_averages()
+    leaderboard_processor.generate_model_rankings()
+    # leaderboard_processor.apply_rankings_to_dataframes() # This might be redundant if generate_model_rankings covers it
+    # leaderboard_processor.format_dataframes()
+    logger.info("Leaderboard data processing complete.")
+
+    # Create the main Gradio interface using gr.Blocks
+    with gr.Blocks(title="MIZAN: A Persian LLM Leaderboard") as demo:
+        gr.Markdown("<h1 style='text-align: center; width: 100%; margin-bottom: 10px;'>🇮🇷 MIZAN: A Persian LLM Leaderboard</h1>")
+        gr.Markdown("""<p style='font-size: 1.1em; text-align: center; max-width: 800px; margin: 0 auto 20px auto;'>
+        MIZAN: A Persian LLM Leaderboard is a comprehensive benchmark for evaluating Large Language Models (LLMs) in Persian.
+        It combines existing datasets, translated benchmarks, and new Persian-specific data to assess LLM capabilities in understanding,
+        generation, reasoning, and knowledge relevant to the Persian language and culture.
+        MIZAN provides a standardized tool for researchers and developers to measure Persian LLM performance.
+        </p>""")
+
+        with gr.Tabs():
+            with gr.TabItem("LLM Benchmark"):
+                logger.info("Creating 'LLM Benchmark' tab content...")
+                # Embed the leaderboard interface generated by LeaderboardApp
+                # The create_gradio_interface method of LeaderboardApp should return a gr.Blocks or gr.Interface instance
+                leaderboard_processor.create_gradio_interface() # This directly adds its components to the current gr.Blocks scope
+                logger.info("'LLM Benchmark' tab content created.")
+
+            with gr.TabItem("About MIZAN"): # Changed from "About PULL"
+                logger.info("Creating 'About MIZAN' tab content...") # Changed from "About PULL"
+                render_about() # Call the function that returns the 'About' page Blocks
+                logger.info("'About MIZAN' tab content created.") # Changed from "About PULL"
+
+            with gr.TabItem("Request New Model"):
+                logger.info("Creating 'Submit Your Model' tab content...")
+                render_submit() # Call the function that returns the 'Submit' page Blocks
+                logger.info("'Submit Your Model' tab content created.")
+
+    logger.info("MIZAN: A Persian LLM Leaderboard application interface created.") # Changed from "PULL Leaderboard"
+    return demo
+
+if __name__ == "__main__":
+    logger.info("Launching MIZAN: A Persian LLM Leaderboard application...") # Changed from "PULL Leaderboard"
+    pull_app = create_app() # Variable name 'pull_app' kept as is, but can be changed if desired e.g., to 'mizan_app'
+    pull_app.launch(
+        debug=True, # Enable Gradio debug mode for more detailed error messages in development
+        # share=True # Uncomment to create a public link (useful for temporary sharing)
+        # server_name="0.0.0.0" # Uncomment to make accessible on your local network
+    )
+    logger.info("MIZAN: A Persian LLM Leaderboard application has been launched.") # Changed from "PULL Leaderboard"
+# Ensure there are no hidden/invalid characters after this line. A single newline character is standard.

leaderboard/boards_data/MMLU.jsonl

@@ -0,0 +1,29 @@
+{"Model Name":"deepseek-reasoner","model_url":"https_google.com","parameters_count":"671000000000","source_type":"Open-Source","acc":0.7808162132,"cinema_acc":0.425,"emergency_number_acc":0.6,"foods_acc":0.65,"games_acc":0.55,"herbal_drugs_acc":0.675,"places_acc":0.8476190476,"poetry_acc":0.625,"politicians_acc":0.9,"popular_people_acc":0.7897435897,"Government_law_acc":0.8913043478,"proverbs_acc":0.76,"religous_acc":0.9333333333,"social_manners_acc":0.8426966292,"souvenirs_acc":0.72,"sports_acc":0.4761904762,"GPK_acc":0.7268195414,"SPK_acc":0.7799677477,"UPK_acc":0.8758064516}
+{"Model Name":"claude-3-7-sonnet-20250219","model_url":"https_google.com","parameters_count":"N\/A","source_type":"Closed-Source","acc":0.7214996174,"cinema_acc":0.6875,"emergency_number_acc":0.7,"foods_acc":0.74,"games_acc":0.5,"herbal_drugs_acc":0.675,"places_acc":0.8476190476,"poetry_acc":0.8,"politicians_acc":0.95,"popular_people_acc":0.8615384615,"Government_law_acc":0.8913043478,"proverbs_acc":0.77,"religous_acc":0.9333333333,"social_manners_acc":0.9101123596,"souvenirs_acc":0.72,"sports_acc":0.6031746032,"GPK_acc":0.7936191426,"SPK_acc":0.7588245834,"UPK_acc":0.6854869755}
+{"Model Name":"gpt-4.1","model_url":"https_google.com","parameters_count":"N\/A","source_type":"Closed-Source","acc":0.7040411769,"cinema_acc":0.7625,"emergency_number_acc":0.9,"foods_acc":0.78,"games_acc":0.7,"herbal_drugs_acc":0.625,"places_acc":0.8666666667,"poetry_acc":0.875,"politicians_acc":0.85,"popular_people_acc":0.8461538462,"Government_law_acc":0.8913043478,"proverbs_acc":0.86,"religous_acc":0.8888888889,"social_manners_acc":0.8651685393,"souvenirs_acc":0.68,"sports_acc":0.4761904762,"GPK_acc":0.8005982054,"SPK_acc":0.7258555814,"UPK_acc":0.6759912742}
+{"Model Name":"gemini-2.0-flash","model_url":"https_google.com","parameters_count":"N\/A","source_type":"Closed-Source","acc":0.6957640676,"cinema_acc":0.725,"emergency_number_acc":0.6,"foods_acc":0.79,"games_acc":0.5,"herbal_drugs_acc":0.75,"places_acc":0.8380952381,"poetry_acc":0.825,"politicians_acc":0.75,"popular_people_acc":0.7846153846,"Government_law_acc":0.9565217391,"proverbs_acc":0.78,"religous_acc":0.9111111111,"social_manners_acc":0.8539325843,"souvenirs_acc":0.74,"sports_acc":0.4761904762,"GPK_acc":0.7756729811,"SPK_acc":0.7263931195,"UPK_acc":0.6635442063}
+{"Model Name":"gpt-4o","model_url":"https_google.com","parameters_count":"N\/A","source_type":"Closed-Source","acc":0.6884607359,"cinema_acc":0.75,"emergency_number_acc":0.7,"foods_acc":0.78,"games_acc":0.6,"herbal_drugs_acc":0.7,"places_acc":0.8380952381,"poetry_acc":0.9,"politicians_acc":0.95,"popular_people_acc":0.8615384615,"Government_law_acc":0.9347826087,"proverbs_acc":0.8,"religous_acc":0.9333333333,"social_manners_acc":0.8426966292,"souvenirs_acc":0.66,"sports_acc":0.5555555556,"GPK_acc":0.8015952144,"SPK_acc":0.720121842,"UPK_acc":0.6512254587}
+{"Model Name":"gpt-4.1-mini","model_url":"https_google.com","parameters_count":"N\/A","source_type":"Closed-Source","acc":0.6128538638,"cinema_acc":0.525,"emergency_number_acc":0.7,"foods_acc":0.73,"games_acc":0.55,"herbal_drugs_acc":0.625,"places_acc":0.8380952381,"poetry_acc":0.575,"politicians_acc":0.6,"popular_people_acc":0.7076923077,"Government_law_acc":0.847826087,"proverbs_acc":0.71,"religous_acc":0.6666666667,"social_manners_acc":0.8202247191,"souvenirs_acc":0.68,"sports_acc":0.4920634921,"GPK_acc":0.6949152542,"SPK_acc":0.6265902168,"UPK_acc":0.5924547671}
+{"Model Name":"c4ai-command-a-03-2025","model_url":"https_google.com","parameters_count":"111000000000","source_type":"Open-Source","acc":0.5980651448,"cinema_acc":0.6,"emergency_number_acc":0.5,"foods_acc":0.67,"games_acc":0.65,"herbal_drugs_acc":0.675,"places_acc":0.8476190476,"poetry_acc":0.775,"politicians_acc":0.95,"popular_people_acc":0.8092783505,"Government_law_acc":0.8913043478,"proverbs_acc":0.78,"religous_acc":0.8666666667,"social_manners_acc":0.8988764045,"souvenirs_acc":0.68,"sports_acc":0.5396825397,"GPK_acc":0.7604790419,"SPK_acc":0.6417428725,"UPK_acc":0.5458980614}
+{"Model Name":"deepseek-chat","model_url":"https_google.com","parameters_count":"671000000000","source_type":"Open-Source","acc":0.5908047576,"cinema_acc":0.5875,"emergency_number_acc":0.4,"foods_acc":0.56,"games_acc":0.55,"herbal_drugs_acc":0.75,"places_acc":0.8285714286,"poetry_acc":0.75,"politicians_acc":0.7,"popular_people_acc":0.7794871795,"Government_law_acc":0.8695652174,"proverbs_acc":0.78,"religous_acc":0.8444444444,"social_manners_acc":0.808988764,"souvenirs_acc":0.74,"sports_acc":0.5555555556,"GPK_acc":0.7288135593,"SPK_acc":0.6348324673,"UPK_acc":0.541511613}
+{"Model Name":"Llama-3.3-70B-Instruct","model_url":"https_google.com","parameters_count":"70600000000","source_type":"Open-Source","acc":0.5714086374,"cinema_acc":0.5625,"emergency_number_acc":0.3,"foods_acc":0.56,"games_acc":0.6,"herbal_drugs_acc":0.575,"places_acc":0.8095238095,"poetry_acc":0.6,"politicians_acc":0.85,"popular_people_acc":0.7282051282,"Government_law_acc":0.8913043478,"proverbs_acc":0.7,"religous_acc":0.8222222222,"social_manners_acc":0.8539325843,"souvenirs_acc":0.6,"sports_acc":0.5555555556,"GPK_acc":0.6939182453,"SPK_acc":0.605489774,"UPK_acc":0.5310727179}
+{"Model Name":"gpt-4o-mini","model_url":"https_google.com","parameters_count":"N\/A","source_type":"Closed-Source","acc":0.56986854,"cinema_acc":0.6625,"emergency_number_acc":0.4,"foods_acc":0.71,"games_acc":0.45,"herbal_drugs_acc":0.675,"places_acc":0.7714285714,"poetry_acc":0.675,"politicians_acc":0.75,"popular_people_acc":0.6820512821,"Government_law_acc":0.8913043478,"proverbs_acc":0.75,"religous_acc":0.7777777778,"social_manners_acc":0.7865168539,"souvenirs_acc":0.68,"sports_acc":0.5555555556,"GPK_acc":0.7078763709,"SPK_acc":0.6075972048,"UPK_acc":0.5250866162}
+{"Model Name":"Qwen3-32B","model_url":"https_google.com","parameters_count":"32800000000","source_type":"Open-Source","acc":0.5635086255,"cinema_acc":0.45,"emergency_number_acc":0.2,"foods_acc":0.49,"games_acc":0.45,"herbal_drugs_acc":0.45,"places_acc":0.6285714286,"poetry_acc":0.35,"politicians_acc":0.3,"popular_people_acc":0.4974358974,"Government_law_acc":0.7608695652,"proverbs_acc":0.64,"religous_acc":0.6888888889,"social_manners_acc":0.8202247191,"souvenirs_acc":0.56,"sports_acc":0.3968253968,"GPK_acc":0.5513459621,"SPK_acc":0.5967741935,"UPK_acc":0.5412549724}
+{"Model Name":"gemma-3-27b-it","model_url":"https_google.com","parameters_count":"27400000000","source_type":"Open-Source","acc":0.5633303193,"cinema_acc":0.625,"emergency_number_acc":0.4,"foods_acc":0.68,"games_acc":0.35,"herbal_drugs_acc":0.6,"places_acc":0.7904761905,"poetry_acc":0.7,"politicians_acc":0.75,"popular_people_acc":0.641025641,"Government_law_acc":0.8913043478,"proverbs_acc":0.74,"religous_acc":0.7777777778,"social_manners_acc":0.8764044944,"souvenirs_acc":0.62,"sports_acc":0.6031746032,"GPK_acc":0.6989032901,"SPK_acc":0.5977423401,"UPK_acc":0.5212370076}
+{"Model Name":"claude-3-5-haiku-20241022","model_url":"https_google.com","parameters_count":"N\/A","source_type":"Closed-Source","acc":0.5440356745,"cinema_acc":0.5263157895,"emergency_number_acc":0.4,"foods_acc":0.72,"games_acc":0.55,"herbal_drugs_acc":0.575,"places_acc":0.8095238095,"poetry_acc":0.625,"politicians_acc":0.75,"popular_people_acc":0.6717948718,"Government_law_acc":0.8043478261,"proverbs_acc":0.72,"religous_acc":0.8444444444,"social_manners_acc":0.8539325843,"souvenirs_acc":0.6,"sports_acc":0.4920634921,"GPK_acc":0.6906906907,"SPK_acc":0.5934420355,"UPK_acc":0.4897066392}
+{"Model Name":"Qwen3-14B","model_url":"https_google.com","parameters_count":"14800000000","source_type":"Open-Source","acc":0.5139458858,"cinema_acc":0.55,"emergency_number_acc":0.4,"foods_acc":0.52,"games_acc":0.5,"herbal_drugs_acc":0.5,"places_acc":0.6285714286,"poetry_acc":0.5,"politicians_acc":0.6,"popular_people_acc":0.5641025641,"Government_law_acc":0.7173913043,"proverbs_acc":0.5,"religous_acc":0.6444444444,"social_manners_acc":0.7415730337,"souvenirs_acc":0.28,"sports_acc":0.5396825397,"GPK_acc":0.5623130608,"SPK_acc":0.5513348862,"UPK_acc":0.4809444373}
+{"Model Name":"gemma-3-12b-it","model_url":"https_google.com","parameters_count":"12200000000","source_type":"Open-Source","acc":0.5105376643,"cinema_acc":0.5,"emergency_number_acc":0.5,"foods_acc":0.53,"games_acc":0.4,"herbal_drugs_acc":0.625,"places_acc":0.6857142857,"poetry_acc":0.575,"politicians_acc":0.65,"popular_people_acc":0.6205128205,"Government_law_acc":0.8043478261,"proverbs_acc":0.61,"religous_acc":0.6,"social_manners_acc":0.7078651685,"souvenirs_acc":0.54,"sports_acc":0.5714285714,"GPK_acc":0.6091724826,"SPK_acc":0.556710267,"UPK_acc":0.4647760811}
+{"Model Name":"Qwen3-30B-A3B","model_url":"https_google.com","parameters_count":"30500000000","source_type":"Open-Source","acc":0.5097725534,"cinema_acc":0.4875,"emergency_number_acc":0.3,"foods_acc":0.5,"games_acc":0.5,"herbal_drugs_acc":0.4,"places_acc":0.5428571429,"poetry_acc":0.45,"politicians_acc":0.4,"popular_people_acc":0.6051282051,"Government_law_acc":0.7826086957,"proverbs_acc":0.63,"religous_acc":0.7333333333,"social_manners_acc":0.6853932584,"souvenirs_acc":0.34,"sports_acc":0.5079365079,"GPK_acc":0.5593220339,"SPK_acc":0.5384339724,"UPK_acc":0.4828692416}
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leaderboard/boards_data/all.jsonl

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leaderboard/boards_data/extractive-qa_PQuAD.jsonl

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leaderboard/boards_data/keyword-extraction_SynKeywords.jsonl

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leaderboard/boards_data/mt_bench.jsonl

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leaderboard/boards_data/ner_arman.jsonl

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leaderboard/boards_data/nli_farstail.jsonl

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leaderboard/boards_data/paraphrase-detection_parsinlu.jsonl

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leaderboard/boards_data/question-generation_PersianQA.jsonl

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leaderboard/boards_data/sentiment-analysis_deepsentipers.jsonl

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leaderboard/boards_data/sts_SynPerSTS.jsonl

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leaderboard/boards_data/summarization_PnSummary.jsonl

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leaderboard/boards_data/summarization_SamSUM-fa.jsonl

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leaderboard/boards_data/tone-classification_SynTone.jsonl

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leaderboard/boards_data/translation-ar2fa_ar2fa.jsonl

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leaderboard/boards_data/translation-en2fa_en2fa.jsonl

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leaderboard/boards_data/translation-fa2en_fa2en.jsonl

@@ -0,0 +1,29 @@
+{"Model Name":"claude-3-7-sonnet-20250219","model_url":"https:\/\/google.com","parameters_count":"None","source_type":"Closed-Source","translation-fa2en_fa2en_bleu":0.2247897554,"translation-fa2en_fa2en_tep_bleu":0.1341840946,"translation-fa2en_fa2en_mizan_bleu":0.1909021288,"translation-fa2en_fa2en_quran_bleu":0.1740971535,"translation-fa2en_fa2en_epoque_bleu":0.4544315204,"translation-fa2en_fa2en_nahj_bleu":0.0877235615,"translation-fa2en_fa2en_sahife_bleu":0.0975791022,"nlg_score":0.1779340777}
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+{"Model Name":"gemini-2.0-flash","model_url":"https:\/\/google.com","parameters_count":"None","source_type":"Closed-Source","translation-fa2en_fa2en_bleu":0.0757086487,"translation-fa2en_fa2en_tep_bleu":0.0316922994,"translation-fa2en_fa2en_mizan_bleu":0.0530331645,"translation-fa2en_fa2en_quran_bleu":0.1028139165,"translation-fa2en_fa2en_epoque_bleu":0.157367237,"translation-fa2en_fa2en_nahj_bleu":0.0336372263,"translation-fa2en_fa2en_sahife_bleu":0.0279485156,"nlg_score":0.178231145}
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+{"Model Name":"gpt-4.1-nano","model_url":"https:\/\/google.com","parameters_count":"None","source_type":"Closed-Source","translation-fa2en_fa2en_bleu":0.2165819036,"translation-fa2en_fa2en_tep_bleu":0.13491043,"translation-fa2en_fa2en_mizan_bleu":0.1810957829,"translation-fa2en_fa2en_quran_bleu":0.164168601,"translation-fa2en_fa2en_epoque_bleu":0.4383628208,"translation-fa2en_fa2en_nahj_bleu":0.0942939662,"translation-fa2en_fa2en_sahife_bleu":0.0827637394,"nlg_score":0.1665903777}
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+{"Model Name":"gpt-4o","model_url":"https:\/\/google.com","parameters_count":"None","source_type":"Closed-Source","translation-fa2en_fa2en_bleu":0.234039473,"translation-fa2en_fa2en_tep_bleu":0.1597644653,"translation-fa2en_fa2en_mizan_bleu":0.1946759365,"translation-fa2en_fa2en_quran_bleu":0.1638938233,"translation-fa2en_fa2en_epoque_bleu":0.474760879,"translation-fa2en_fa2en_nahj_bleu":0.0825458621,"translation-fa2en_fa2en_sahife_bleu":0.0952634494,"nlg_score":0.18964968}
+{"Model Name":"deepseek-reasoner","model_url":"https:\/\/google.com","parameters_count":"671000000000","source_type":"Open-Source","translation-fa2en_fa2en_bleu":0.0414094379,"translation-fa2en_fa2en_tep_bleu":0.019539618,"translation-fa2en_fa2en_mizan_bleu":0.0346087447,"translation-fa2en_fa2en_quran_bleu":0.0396858881,"translation-fa2en_fa2en_epoque_bleu":0.0798341141,"translation-fa2en_fa2en_nahj_bleu":0.0244191809,"translation-fa2en_fa2en_sahife_bleu":0.0231626908,"nlg_score":0.0880621978}
+{"Model Name":"Qwen3-30B-A3B","model_url":"https:\/\/google.com","parameters_count":"30500000000","source_type":"Open-Source","translation-fa2en_fa2en_bleu":0.2177785793,"translation-fa2en_fa2en_tep_bleu":0.1189948472,"translation-fa2en_fa2en_mizan_bleu":0.1793626928,"translation-fa2en_fa2en_quran_bleu":0.1718006478,"translation-fa2en_fa2en_epoque_bleu":0.4500382308,"translation-fa2en_fa2en_nahj_bleu":0.0836776138,"translation-fa2en_fa2en_sahife_bleu":0.1034067477,"nlg_score":0.164118288}
+{"Model Name":"Llama-3.2-3B-Instruct","model_url":"https:\/\/google.com","parameters_count":"3210000000","source_type":"Open-Source","translation-fa2en_fa2en_bleu":0.0862123314,"translation-fa2en_fa2en_tep_bleu":0.0334491641,"translation-fa2en_fa2en_mizan_bleu":0.0758837027,"translation-fa2en_fa2en_quran_bleu":0.0892296624,"translation-fa2en_fa2en_epoque_bleu":0.1688644918,"translation-fa2en_fa2en_nahj_bleu":0.042819328,"translation-fa2en_fa2en_sahife_bleu":0.0473482715,"nlg_score":0.1129755187}
+{"Model Name":"Llama-3.2-1B-Instruct","model_url":"https:\/\/google.com","parameters_count":"1240000000","source_type":"Open-Source","translation-fa2en_fa2en_bleu":0.0423299736,"translation-fa2en_fa2en_tep_bleu":0.0124774953,"translation-fa2en_fa2en_mizan_bleu":0.0314077643,"translation-fa2en_fa2en_quran_bleu":0.0294898862,"translation-fa2en_fa2en_epoque_bleu":0.1006673489,"translation-fa2en_fa2en_nahj_bleu":0.0117672852,"translation-fa2en_fa2en_sahife_bleu":0.0246608556,"nlg_score":0.0823387318}

leaderboard/leaderboard.py

@@ -0,0 +1,605 @@
+# leaderboard/leaderboard.py
+import gradio as gr
+import pandas as pd
+import logging
+from pathlib import Path
+import yaml
+from typing import Dict, List, Union, Optional, Any
+import numpy as np
+
+# --- Logging Setup ---
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s - %(levelname)s - %(module)s - %(message)s"
+)
+logger = logging.getLogger(__name__)
+
+# --- Path Definitions ---
+LEADERBOARD_DIR = Path(__file__).resolve().parent
+CONFIG_FILE_PATH = LEADERBOARD_DIR / "leaderboard_config.yaml"
+DATA_DIR = LEADERBOARD_DIR / "boards_data"
+
+class ColumnConfig:
+    
+    def __init__(self, config_path: Path):
+        self.config_path = config_path
+        self.column_display_names_map: Dict[str, str] = {}
+        self.task_tab_names_map: Dict[str, str] = {}
+
+        default_task_tab_names = {
+            "all": "Overall", "mt_bench": "MT-Bench", "ifeval": "IFEval",
+            "MMLU": "MMLU", "persian_csr": "PerCoR",
+            "persian_nlg": "Persian NLG", "persian_nlu": "Persian NLU"
+        }
+        default_column_names = {
+            "Model Name": "Model", "model_url": "URL",
+            "parameters_count": "⚙️ Params", "source_type": "Source",
+            "Average": "Average", "Rank": "🏆 Rank", "score_mean": "score_mean (main)",
+            "strict_instruction_accuracy": "strict_instruction_accuracy (main)", "acc": "accuracy (main)",
+            "nlg_score": "nlg_score (main)", "nlu_score": "nlu_score (main)",
+        }
+
+        if self.config_path and self.config_path.exists():
+            try:
+                with open(self.config_path, 'r', encoding='utf-8') as f:
+                    config = yaml.safe_load(f)
+                    loaded_column_names = config.get('column_names', {})
+                    self.column_display_names_map = {**default_column_names, **loaded_column_names}
+                    loaded_task_names = config.get('task_display_names', {})
+                    self.task_tab_names_map = {**default_task_tab_names, **loaded_task_names}
+            except Exception as e:
+                logger.error(f"Error loading UI name configurations from {self.config_path}: {e}. Using defaults.")
+                self.column_display_names_map = default_column_names
+                self.task_tab_names_map = default_task_tab_names
+        else:
+            logger.warning(f"UI Name configuration file '{self.config_path.name}' not found. Using defaults.")
+            self.column_display_names_map = default_column_names
+            self.task_tab_names_map = default_task_tab_names
+
+    def get_column_display_name(self, original_col_name: str) -> str:
+        return self.column_display_names_map.get(original_col_name, original_col_name.replace("_", " "))
+
+    def get_task_tab_name(self, task_key: str) -> str:
+        return self.task_tab_names_map.get(task_key, task_key.replace("_", " "))
+
+    def rename_dataframe_columns(self, df: pd.DataFrame) -> pd.DataFrame:
+        if df.empty: return df
+        rename_dict = {col: self.get_column_display_name(col) for col in df.columns}
+        return df.rename(columns=rename_dict)
+
+
+class LeaderboardApp:
+    def __init__(self, config_path: Path):
+        self.config_path = config_path
+        self.column_config = ColumnConfig(config_path)
+        self.raw_dataframes: Dict[str, pd.DataFrame] = {}
+        self.model_display_configs: Dict[str, Dict[str, str]] = {}
+
+        self.model_identifier_column: str = "Model Name"
+        self.main_scores_map: Dict[str, str] = {}
+        self.allowed_null_columns_in_average: List[str] = ["Model Name", "model_url", "parameters_count", "source_type"]
+        self.tab_processing_order: List[str] = []
+        self.numeric_score_columns_for_bolding: List[str] = []
+        self.columns_to_hide: List[str] = ["model_url", "source_type"]
+        self.parent_child_task_map: Dict[str, List[str]] = {}
+
+        self._load_global_settings()
+        self._load_model_display_configs()
+
+    def _load_global_settings(self) -> None:
+        # ... (بدون تغییر نسبت به نسخه قبلی شما) ...
+        if self.config_path and self.config_path.exists():
+            try:
+                with open(self.config_path, 'r', encoding='utf-8') as f:
+                    config = yaml.safe_load(f)
+                    settings = config.get('global_settings', {})
+                    self.model_identifier_column = settings.get('model_identifier_column', self.model_identifier_column)
+                    self.main_scores_map = settings.get('main_scores_map', self.main_scores_map)
+                    self.allowed_null_columns_in_average = settings.get('allowed_null_columns_in_average', self.allowed_null_columns_in_average)
+                    self.tab_processing_order = settings.get('tab_processing_order', [])
+                    self.columns_to_hide = settings.get('columns_to_hide', self.columns_to_hide)
+                    self.parent_child_task_map = settings.get('parent_child_task_map', {})
+
+                    default_numeric_bold_cols = list(self.main_scores_map.values()) if self.main_scores_map else []
+                    self.numeric_score_columns_for_bolding = settings.get('numeric_score_columns_for_bolding', default_numeric_bold_cols)
+                    if not self.numeric_score_columns_for_bolding and default_numeric_bold_cols:
+                        self.numeric_score_columns_for_bolding = default_numeric_bold_cols
+                    if 'all' in self.main_scores_map and self.main_scores_map.get('all') and \
+                       self.main_scores_map['all'] not in self.numeric_score_columns_for_bolding:
+                        self.numeric_score_columns_for_bolding.append(self.main_scores_map['all'])
+                    self.numeric_score_columns_for_bolding = list(set(self.numeric_score_columns_for_bolding))
+            except Exception as e:
+                logger.error(f"Error loading global settings from {self.config_path}: {e}. Using defaults.")
+        else:
+            logger.error(f"Main configuration file '{getattr(self.config_path, 'name', 'config_path')}' not found. Critical settings will use defaults.")
+
+
+    def _load_model_display_configs(self) -> None:
+        
+        if self.config_path and self.config_path.exists():
+            try:
+                with open(self.config_path, 'r', encoding='utf-8') as f:
+                    config = yaml.safe_load(f)
+                    self.model_display_configs = config.get('model_display_configs', {})
+            except Exception as e:
+                logger.error(f"Error loading model display configs: {e}")
+        else:
+            logger.warning(f"Model display config section not found in {self.config_path}.")
+
+    def load_data(self) -> None:
+        
+        logger.info(f"Loading all data from: {DATA_DIR}")
+        if not DATA_DIR.exists() or not DATA_DIR.is_dir():
+            logger.error(f"Data directory {DATA_DIR} not found. Cannot load data.")
+            return
+
+        all_jsonl_files = list(DATA_DIR.glob("*.jsonl"))
+        if not all_jsonl_files:
+            logger.warning(f"No .jsonl files found in {DATA_DIR}. No data will be loaded.")
+            return
+
+        for file_path in all_jsonl_files:
+            task_key = file_path.stem
+            try:
+                self.raw_dataframes[task_key] = pd.read_json(file_path, lines=True)
+                logger.info(f"Successfully loaded '{file_path.name}' for task key '{task_key}'.")
+            except Exception as e:
+                self.raw_dataframes[task_key] = pd.DataFrame()
+                logger.error(f"Error loading '{file_path.name}' for task '{task_key}': {e}")
+
+        configured_task_keys = set(self.tab_processing_order)
+        for parent, children in self.parent_child_task_map.items():
+            configured_task_keys.add(parent)
+            if children:
+                configured_task_keys.update(children)
+
+        for key in configured_task_keys:
+            if key not in self.raw_dataframes:
+                self.raw_dataframes[key] = pd.DataFrame()
+                logger.warning(f"No data file found for configured task key '{key}'. Initialized as empty.")
+
+    def _get_benchmark_columns(self, df: pd.DataFrame) -> List[str]:
+        
+        if df.empty: return []
+        excluded_cols = self.allowed_null_columns_in_average + ["Rank", "model_url", "Average"]
+        return [col for col in df.columns if col not in excluded_cols and pd.api.types.is_numeric_dtype(df[col])]
+
+    def handle_nulls_in_averages(self) -> None:
+        
+        logger.info("Skipping handle_nulls_in_averages as refresh.py is expected to handle it.")
+        pass
+
+    def _calculate_non_null_benchmark_score_count(self, df_row: pd.Series, benchmark_cols: List[str]) -> int:
+        
+        return df_row[benchmark_cols].notna().sum()
+
+    def generate_model_rankings(self) -> None:
+        # ... (بدون تغییر - منطق فعلی به درستی مدل‌های بدون میانگین را به پایین می‌برد) ...
+        logger.info("Generating model rankings for each tab.")
+        if not self.model_identifier_column:
+            logger.error("`model_identifier_column` is not set. Cannot perform ranking.")
+            return
+        for task_key, df in self.raw_dataframes.items():
+            if df.empty: continue
+            ranked_df = df.copy()
+            main_score_col_for_tab = self.main_scores_map.get(task_key)
+            if not main_score_col_for_tab or main_score_col_for_tab not in ranked_df.columns:
+                logger.warning(f"No main score column for task '{task_key}'. Ranking skipped.")
+                ranked_df["Rank"] = pd.NA # Use pd.NA for missing ranks
+                self.raw_dataframes[task_key] = ranked_df
+                continue
+            ranked_df[main_score_col_for_tab] = pd.to_numeric(ranked_df[main_score_col_for_tab], errors='coerce')
+            ranked_df['_has_main_score'] = ranked_df[main_score_col_for_tab].notna()
+            ranked_df['_sortable_main_score'] = ranked_df[main_score_col_for_tab].fillna(-np.inf)
+            sort_by_cols = ['_has_main_score', '_sortable_main_score', self.model_identifier_column]
+            ascending_order = [False, False, True]
+            ranked_df = ranked_df.sort_values(by=sort_by_cols, ascending=ascending_order, na_position='last')
+            # Assign ranks only to rows that have a main score; others get NA
+            ranked_df["Rank"] = pd.NA
+            ranked_df.loc[ranked_df['_has_main_score'], "Rank"] = range(1, ranked_df['_has_main_score'].sum() + 1)
+
+            ranked_df.drop(columns=['_has_main_score', '_sortable_main_score'], inplace=True)
+            self.raw_dataframes[task_key] = ranked_df
+            logger.info(f"Generated rankings for {task_key}.")
+
+
+    @staticmethod
+    def _format_value_as_percentage(value: Any, score_cutoff_for_percentage: float = 0.0) -> Any:
+        # ... (بدون تغییر - این متد ممکن است جای دیگری استفاده شود) ...
+        if pd.isna(value) or not isinstance(value, (int, float)): return value
+        if value >= score_cutoff_for_percentage and 0 <= value <= 1.0: return f"{value * 100:.2f}%"
+        return f"{value:.2f}" if isinstance(value, float) else value
+
+    @staticmethod
+    def _format_parameters_count(value: Any) -> str:
+        
+        if pd.isna(value) or str(value).lower() in ["n/a", "unknown", "", "none"]: return "Unknown"
+        try:
+            num_value = float(value)
+            if num_value == 0: return "N/A"
+            if num_value >= 1_000_000_000: return f"{num_value / 1_000_000_000:.1f}B"
+            if num_value >= 1_000_000: return f"{num_value / 1_000_000:.1f}M"
+            if num_value >= 1_000: return f"{num_value / 1_000:.1f}K"
+            return str(int(num_value))
+        except ValueError: return str(value)
+
+    def _apply_general_formatting_to_cells(self, df_to_format: pd.DataFrame, task_key: str) -> pd.DataFrame:
+        if df_to_format.empty:
+            return df_to_format
+
+        formatted_df = df_to_format.copy()
+        is_mt_bench_tab = (str(task_key).lower() == "mt_bench")
+
+        for col_name in formatted_df.columns:
+            if col_name == "parameters_count":
+                formatted_df[col_name] = formatted_df[col_name].apply(self._format_parameters_count)
+                continue
+
+            if col_name == "Rank": # Rank should typically be integer or NA, no special formatting here
+                # Convert Rank to integer if possible, otherwise keep as is (e.g. for NA)
+                try:
+                    # Attempt to convert to Int64 to handle pd.NA
+                    formatted_df[col_name] = formatted_df[col_name].astype(pd.Int64Dtype())
+                except Exception:
+                    pass # If conversion fails, leave as is
+                continue
+
+
+            new_col_values = []
+            for x_cell_value in formatted_df[col_name]:
+                original_value_for_cell = x_cell_value
+
+                numeric_x = x_cell_value
+                is_cell_numeric_type = isinstance(x_cell_value, (int, float, np.number))
+
+                if not is_cell_numeric_type:
+                    try:
+                        numeric_x = pd.to_numeric(x_cell_value)
+                        is_cell_numeric_type = True
+                    except ValueError:
+                        is_cell_numeric_type = False
+
+                if pd.isna(numeric_x):
+                    new_col_values.append("") # Display NaNs as empty strings
+                    continue
+
+                formatted_cell_value = original_value_for_cell
+
+                if is_cell_numeric_type:
+                    if is_mt_bench_tab: # Special handling for mt_bench tab
+                        if isinstance(numeric_x, float):
+                            formatted_cell_value = f"{numeric_x:.2f}"
+                        else:
+                            formatted_cell_value = numeric_x
+                    else: # For all other tabs
+                        if isinstance(numeric_x, (int, float)) and 0 <= numeric_x <= 1.0:
+                            val_multiplied = numeric_x * 100
+                            # If original was 0 or 1 (resulting in 0 or 100), format as integer
+                            if numeric_x == 1.0 or numeric_x == 0.0:
+                                formatted_cell_value = f"{val_multiplied:.0f}" # "100" or "0"
+                            else:
+                                # Otherwise, format to 2 decimal places (e.g., 88.00, 75.50)
+                                formatted_cell_value = f"{val_multiplied:.2f}"
+                        elif isinstance(numeric_x, float):
+                            formatted_cell_value = f"{numeric_x:.2f}"
+                        else: # Integers outside 0-1 range, etc.
+                            formatted_cell_value = numeric_x
+
+                new_col_values.append(formatted_cell_value)
+            formatted_df[col_name] = new_col_values
+        return formatted_df
+
+    def _apply_markdown_and_bolding(self, df_with_general_formats: pd.DataFrame) -> pd.DataFrame:
+        # ... (بدون تغییر نسبت به نسخه قبلی شما) ...
+        if df_with_general_formats.empty: return df_with_general_formats
+        formatted_df = df_with_general_formats.copy()
+
+        model_id_col_original = self.model_identifier_column
+
+        if model_id_col_original in formatted_df.columns and 'model_url' in formatted_df.columns:
+            def create_markdown_link(row):
+                model_id_val = row[model_id_col_original]
+                url = row['model_url']
+
+                display_conf = self.model_display_configs.get(str(model_id_val), {})
+                display_name = display_conf.get('display_name', str(model_id_val))
+                url_for_link = display_conf.get('url', url if pd.notna(url) else 'https://google.com')
+                if not url_for_link or pd.isna(url_for_link): url_for_link = 'https://google.com'
+                return f"[{display_name}]({url_for_link})"
+            formatted_df[model_id_col_original] = formatted_df.apply(create_markdown_link, axis=1)
+
+        for col_name_original in self.numeric_score_columns_for_bolding:
+            if col_name_original in formatted_df.columns:
+                def to_numeric_for_max(val):
+                    if isinstance(val, str):
+                        # Percentage sign is no longer added, so no need to check for it here
+                        # if val.endswith('%'):
+                        #     try: return float(val[:-1])
+                        #     except ValueError: return -np.inf
+                        try: return float(val) # Handles "88.00", "75.50", "100", "0"
+                        except ValueError: return -np.inf
+                    return val if pd.notna(val) else -np.inf
+
+                numeric_series_for_max = formatted_df[col_name_original].apply(to_numeric_for_max)
+
+                if not numeric_series_for_max.empty and numeric_series_for_max.notna().any() and \
+                   pd.api.types.is_numeric_dtype(numeric_series_for_max) and not numeric_series_for_max.eq(-np.inf).all():
+                    max_val_numeric = numeric_series_for_max.max(skipna=True) # Ensure skipna=True for max
+                    if pd.notna(max_val_numeric) and max_val_numeric != -np.inf:
+                        # Iterate using index to ensure correct .loc access
+                        for i in numeric_series_for_max.index:
+                            current_numeric_val = numeric_series_for_max.loc[i]
+                            if pd.notna(current_numeric_val) and current_numeric_val == max_val_numeric:
+                                display_val_to_bold = formatted_df.loc[i, col_name_original]
+                                if not (isinstance(display_val_to_bold, str) and display_val_to_bold.startswith("**") and display_val_to_bold.endswith("**")):
+                                    formatted_df.loc[i, col_name_original] = f"**{display_val_to_bold}**"
+                            elif pd.isna(current_numeric_val) or current_numeric_val == -np.inf:
+                                cell_content = formatted_df.loc[i, col_name_original]
+                                if cell_content is None or \
+                                   (isinstance(cell_content, str) and \
+                                    cell_content.strip().lower() in ["n/a", "", "unknown", "nan"]): # Standardize NA display
+                                    formatted_df.loc[i, col_name_original] = ""
+        return formatted_df
+    # ... (بقیه متدهای LeaderboardApp بدون تغییر باقی می‌مانند، از جمله _get_gr_datatypes, get_prepared_dataframe, make_update_fn_for_task_closure, _create_and_bind_dataframe_component, create_gradio_interface, run_standalone) ...
+
+    @staticmethod
+    def _get_gr_datatypes(df_with_original_cols: pd.DataFrame, model_id_col_original_name: str, score_cols_original_names: List[str]) -> List[str]:
+        datatypes = []
+        if df_with_original_cols.empty: return []
+
+        markdown_cols_original_names = {model_id_col_original_name}
+        markdown_cols_original_names.add("parameters_count")
+        markdown_cols_original_names.update(score_cols_original_names)
+
+        for col_name_original in df_with_original_cols.columns:
+            if col_name_original == "Rank":
+                datatypes.append("number") # Rank can be number or string if NA
+            elif col_name_original in markdown_cols_original_names:
+                datatypes.append("markdown")
+            else:
+                # Most other formatted cells become strings
+                # Checking the dtype of the formatted column can be more robust
+                # For now, default to str for non-markdown, non-rank
+                datatypes.append("str")
+        return datatypes
+
+    def get_prepared_dataframe(self, task_key: str, source_filter: str = "All", name_filter_query: str = "") -> pd.DataFrame:
+        original_df_for_task = self.raw_dataframes.get(task_key)
+        if original_df_for_task is None or original_df_for_task.empty:
+            return pd.DataFrame()
+
+        processed_df = original_df_for_task.copy()
+
+        parent_nlu_nlg_task_keys = ["persian_nlg", "persian_nlu"]
+        if task_key in parent_nlu_nlg_task_keys:
+            cols_to_drop_due_to_object = []
+            for col_name in processed_df.columns:
+                if processed_df[col_name].apply(lambda x: isinstance(x, dict)).any():
+                    cols_to_drop_due_to_object.append(col_name)
+            if cols_to_drop_due_to_object:
+                logger.info(f"For overview task '{task_key}', dropping object columns: {cols_to_drop_due_to_object}")
+                processed_df = processed_df.drop(columns=cols_to_drop_due_to_object, errors='ignore')
+
+        if 'source_type' in processed_df.columns and source_filter != "All":
+            processed_df = processed_df[processed_df['source_type'] == source_filter]
+            if processed_df.empty: return pd.DataFrame()
+
+        if name_filter_query and self.model_identifier_column in processed_df.columns:
+            try:
+                processed_df = processed_df[processed_df[self.model_identifier_column].astype(str).str.contains(name_filter_query, case=False, na=False)]
+            except Exception as e: logger.error(f"Name filter error: {e}")
+            if processed_df.empty: return pd.DataFrame()
+
+        if processed_df.empty: return pd.DataFrame()
+
+        # Apply cell formatting (this now includes the new number formatting rules)
+        processed_df = self._apply_general_formatting_to_cells(processed_df, task_key)
+        # Apply markdown and bolding
+        processed_df = self._apply_markdown_and_bolding(processed_df)
+
+        if self.columns_to_hide:
+            columns_to_drop_existing = [col for col in self.columns_to_hide if col in processed_df.columns]
+            if columns_to_drop_existing:
+                processed_df = processed_df.drop(columns=columns_to_drop_existing, errors='ignore')
+
+        if "Rank" in processed_df.columns:
+            # Ensure Rank is first, if it exists
+            cols_order = ["Rank"] + [col for col in processed_df.columns if col != "Rank"]
+            processed_df = processed_df[cols_order]
+        
+        # Convert Rank to string for display after all operations, to handle NA consistently with other strings
+        if "Rank" in processed_df.columns:
+             processed_df["Rank"] = processed_df["Rank"].apply(lambda x: str(int(x)) if pd.notna(x) and isinstance(x, (float,int)) and x == int(x) else (str(x) if pd.notna(x) else ""))
+
+
+        processed_df = processed_df.fillna("") # Final fillna for display
+        return processed_df
+
+    def make_update_fn_for_task_closure(self, task_key_for_df_data: str):
+        
+        def update_table_data(name_query_str, source_filter_str):
+            logger.debug(f"Updating table for task_key '{task_key_for_df_data}' with name: '{name_query_str}', source: '{source_filter_str}'")
+
+            df_original_cols_formatted_values = self.get_prepared_dataframe(
+                task_key_for_df_data, source_filter_str, name_query_str
+            )
+
+            if df_original_cols_formatted_values.empty:
+                base_raw_df = self.raw_dataframes.get(task_key_for_df_data, pd.DataFrame())
+                base_raw_df_cols = list(base_raw_df.columns) if not base_raw_df.empty else []
+
+                if base_raw_df_cols:
+                    temp_empty_df_orig_cols = pd.DataFrame(columns=base_raw_df_cols)
+                    if self.columns_to_hide:
+                        cols_to_drop_now = [col for col in self.columns_to_hide if col in temp_empty_df_orig_cols.columns]
+                        if cols_to_drop_now:
+                            temp_empty_df_orig_cols = temp_empty_df_orig_cols.drop(columns=cols_to_drop_now)
+
+                    if self.main_scores_map.get(task_key_for_df_data) and "Rank" not in temp_empty_df_orig_cols.columns:
+                         temp_empty_df_orig_cols.insert(0, "Rank", [])
+
+
+                    renamed_empty_df = self.column_config.rename_dataframe_columns(temp_empty_df_orig_cols)
+                    display_headers = list(renamed_empty_df.columns)
+                    gr_datatypes = ["str"] * len(display_headers) if display_headers else ["str"]
+                    return gr.DataFrame(value=pd.DataFrame(columns=display_headers), headers=display_headers if display_headers else ["Info"], datatype=gr_datatypes)
+                else:
+                    info_message = f"No data available for {self.column_config.get_task_tab_name(task_key_for_df_data)} with current filters."
+                    return gr.DataFrame(value=pd.DataFrame([{"Info": info_message}]), headers=["Info"], datatype=["str"])
+
+            gr_datatypes = self._get_gr_datatypes(
+                df_original_cols_formatted_values,
+                self.model_identifier_column,
+                self.numeric_score_columns_for_bolding
+            )
+
+            df_display_cols_formatted_values = self.column_config.rename_dataframe_columns(df_original_cols_formatted_values)
+            display_headers = list(df_display_cols_formatted_values.columns)
+
+            return gr.DataFrame(value=df_display_cols_formatted_values, headers=display_headers, datatype=gr_datatypes)
+        return update_table_data
+
+
+    def _create_and_bind_dataframe_component(self, current_task_key: str, name_search_textbox: gr.Textbox, source_filter_radio: gr.Radio):
+        
+        initial_df_original_cols = self.get_prepared_dataframe(current_task_key, "All", "")
+
+        current_display_headers = []
+        current_datatypes = None
+        df_value_for_gr_display_cols = pd.DataFrame()
+
+        if initial_df_original_cols.empty:
+            base_df = self.raw_dataframes.get(current_task_key, pd.DataFrame())
+            base_df_cols_original = list(base_df.columns) if not base_df.empty else []
+
+            if base_df_cols_original:
+                temp_empty_df_orig_cols = pd.DataFrame(columns=base_df_cols_original)
+                if self.columns_to_hide:
+                    cols_to_drop_now = [col for col in self.columns_to_hide if col in temp_empty_df_orig_cols.columns]
+                    if cols_to_drop_now:
+                        temp_empty_df_orig_cols = temp_empty_df_orig_cols.drop(columns=cols_to_drop_now)
+
+                if self.main_scores_map.get(current_task_key) and "Rank" not in temp_empty_df_orig_cols.columns:
+                    temp_empty_df_orig_cols.insert(0, "Rank", [])
+
+                initial_df_display_cols = self.column_config.rename_dataframe_columns(temp_empty_df_orig_cols)
+                current_display_headers = list(initial_df_display_cols.columns)
+                current_datatypes = ["str"] * len(current_display_headers) if current_display_headers else ["str"]
+                df_value_for_gr_display_cols = pd.DataFrame(columns=current_display_headers)
+            else:
+                current_display_headers = ["Info"]
+                current_datatypes = ["str"]
+                df_value_for_gr_display_cols = pd.DataFrame([{"Info":f"No data or columns configured for {self.column_config.get_task_tab_name(current_task_key)}."}])
+        else:
+            current_datatypes = self._get_gr_datatypes(
+                initial_df_original_cols,
+                self.model_identifier_column,
+                self.numeric_score_columns_for_bolding
+            )
+            initial_df_display_cols = self.column_config.rename_dataframe_columns(initial_df_original_cols)
+            current_display_headers = list(initial_df_display_cols.columns)
+            df_value_for_gr_display_cols = initial_df_display_cols
+
+        df_component = gr.DataFrame(
+            value=df_value_for_gr_display_cols,
+            headers=current_display_headers,
+            datatype=current_datatypes,
+            interactive=False,
+            wrap=True,
+            # height=700,
+            # elem_id=f"dataframe_{current_task_key}"
+        )
+
+        update_fn = self.make_update_fn_for_task_closure(current_task_key)
+        filter_inputs = [name_search_textbox, source_filter_radio]
+
+        name_search_textbox.submit(fn=update_fn, inputs=filter_inputs, outputs=[df_component])
+        source_filter_radio.change(fn=update_fn, inputs=filter_inputs, outputs=[df_component])
+
+        return df_component
+
+    def create_gradio_interface(self) -> gr.Blocks:
+        
+        logger.info("Creating Gradio interface with potentially nested tabs.")
+        with gr.Blocks(theme=gr.themes.Soft(), elem_id="leaderboard_main_container") as leaderboard_ui_blocks:
+            if not self.tab_processing_order and not self.parent_child_task_map:
+                gr.Markdown("### Leaderboard Not Configured\n- `tab_processing_order` and `parent_child_task_map` are not defined or empty in `leaderboard_config.yaml`.")
+                return leaderboard_ui_blocks
+            if not self.raw_dataframes or all(df.empty for df in self.raw_dataframes.values()):
+                 gr.Markdown("### No Data Loaded\n- No data loaded from `boards_data/`. Ensure `refresh.py` ran and JSONL files exist.")
+                 return leaderboard_ui_blocks
+
+            with gr.Row():
+                name_search_textbox = gr.Textbox(label="Search by Model Name", placeholder="Type model name and press Enter...", interactive=True, scale=3)
+                source_filter_radio = gr.Radio(choices=["All", "Open-Source", "Closed-Source"], value="All", label="Filter by Model Source", interactive=True, scale=1)
+
+            with gr.Tabs(elem_id="main_benchmark_tabs") as main_tabs:
+                processed_top_level_keys = set()
+
+                for main_task_key in self.tab_processing_order:
+                    if main_task_key in processed_top_level_keys: continue
+                    processed_top_level_keys.add(main_task_key)
+
+                    main_tab_display_label = self.column_config.get_task_tab_name(main_task_key)
+
+                    with gr.TabItem(label=main_tab_display_label, id=f"main_tab_{main_task_key}"):
+                        gr.Markdown(f"## {main_tab_display_label}")
+
+                        child_task_keys_for_parent = self.parent_child_task_map.get(main_task_key, [])
+
+                        if child_task_keys_for_parent:
+                            with gr.Tabs(elem_id=f"sub_tabs_for_{main_task_key}") as sub_tabs_component:
+                                for child_key in child_task_keys_for_parent:
+                                    if child_key not in self.raw_dataframes or self.raw_dataframes[child_key].empty: # Check if df is empty
+                                        logger.warning(f"Data for sub-task '{child_key}' under parent '{main_task_key}' not loaded or is empty. Skipping sub-tab.")
+                                        child_tab_display_label_empty = self.column_config.get_task_tab_name(child_key)
+                                        with gr.TabItem(label=child_tab_display_label_empty, id=f"sub_tab_{child_key}_empty"):
+                                            gr.Markdown(f"Data for {child_tab_display_label_empty} is not available.")
+                                        continue
+                                    processed_top_level_keys.add(child_key)
+                                    child_tab_display_label = self.column_config.get_task_tab_name(child_key)
+                                    with gr.TabItem(label=child_tab_display_label, id=f"sub_tab_{child_key}"):
+                                        self._create_and_bind_dataframe_component(child_key, name_search_textbox, source_filter_radio)
+                        else: # This main_task_key is a STANDALONE tab
+                            if main_task_key not in self.raw_dataframes or self.raw_dataframes[main_task_key].empty: # Check if df is empty
+                                logger.warning(f"Data for standalone task '{main_task_key}' not loaded or is empty. Skipping tab content.")
+                                gr.Markdown(f"Data for {main_tab_display_label} is not available.")
+                                continue
+                            self._create_and_bind_dataframe_component(main_task_key, name_search_textbox, source_filter_radio)
+            return leaderboard_ui_blocks
+
+    def run_standalone(self) -> None:
+        
+        logger.info("Running LeaderboardApp in standalone mode.")
+        try:
+            self.load_data()
+            if not self.raw_dataframes or all(df.empty for df in self.raw_dataframes.values()):
+                 logger.warning("No data loaded. Leaderboard might be empty or show 'No data' messages.")
+            self.generate_model_rankings()
+            demo_interface = self.create_gradio_interface()
+            demo_interface.launch(server_name="0.0.0.0", server_port=7860, debug=True)
+        except Exception as e:
+            logger.error(f"Error during standalone run: {e}", exc_info=True)
+            try:
+                with gr.Blocks() as error_demo: gr.Error(f"Failed to launch LeaderboardApp: {e}")
+                error_demo.launch(server_name="0.0.0.0", server_port=7860)
+            except Exception as launch_err:
+                 logger.error(f"CRITICAL: Failed even to launch the error Gradio page: {launch_err}")
+
+
+def main():
+    
+    logger.info(f"Initializing LeaderboardApp with config: {CONFIG_FILE_PATH}")
+    if not CONFIG_FILE_PATH.exists():
+        logger.critical(f"CRITICAL: Config file '{CONFIG_FILE_PATH.name}' not found at {CONFIG_FILE_PATH}. App cannot start.")
+        try:
+            with gr.Blocks() as error_demo: gr.Error(f"Config File Not Found: {CONFIG_FILE_PATH}")
+            error_demo.launch(server_name="0.0.0.0", server_port=7860)
+        except Exception as launch_err:
+            logger.error(f"CRITICAL: Failed to launch the error Gradio page for missing config: {launch_err}")
+        return
+    app = LeaderboardApp(config_path=CONFIG_FILE_PATH)
+    app.run_standalone()
+
+if __name__ == '__main__':
+    main()

leaderboard/leaderboard_config.yaml

@@ -0,0 +1,310 @@
+# leaderboard/leaderboard_config.yaml
+
+# --- Column Display Names ---
+# Used to rename columns from your .jsonl files for display in the UI tables.
+# Format: "Original_Column_Name_In_JSONL": "Desired Display Name in UI"
+column_names:
+  # Columns added by the updated refresh.py
+  "Model Name": "Model" # This 'Model Name' is the canonical ID from refresh.py. Its display will be further customized by 'model_display_configs'.
+  "model_url": "Link"
+  "parameters_count": "Parameters"
+  "source_type": "Source Type"
+  "Average": "Average"
+  "score_mean": "score_mean (main)"
+  "strict_instruction_accuracy": "strict_instruction_accuracy (main)"
+  "acc": "accuracy (main)"
+  "nlg_score": "nlg_score (main)"
+  "nlu_score": "nlu_score (main)"
+
+  # Common score columns (these are examples, use your actual metric names from .jsonl)
+  # "Average": "Overall AVG"      # For the 'all' table summary
+  # "strict_instruction_accuracy": "IFEval Acc."
+  # "score_mean": "MT-Bench Score"
+  # "acc": "Accuracy"            # Generic accuracy (e.g., for MMLU, persian_csr)
+  # "nlg_score": "NLG Score"
+  # "nlu_score": "NLU Score"
+  # Add other specific metric columns from your .jsonl files that you want to display or rename
+  # e.g., "exact_match": "Exact Match", "f1": "F1 Score"
+
+# --- Task (Tab) Display Names & Identifiers ---
+# Defines the display names for UI tabs and identifies tasks for processing.
+# Keys MUST match the base filenames of .jsonl files produced by refresh.py (e.g., "MMLU" for "MMLU.jsonl").
+# These keys are also used in 'main_scores_map' and 'tab_processing_order'.
+task_display_names:
+  all: "🏆 Overall Benchmark"
+  mt_bench: "Persian MT-Bench"
+  ifeval: "Persian IFEval"
+  MMLU: "PerMMLU"
+  persian_csr: "PerCoR"
+  persian_nlg: "Persian NLG" # Overview tab
+  persian_nlu: "Persian NLU"
+  question-generation_PersianQA: "PersianQA (QG)"
+  translation-en2fa_en2fa: "Translation (en2fa)"
+  translation-fa2en_fa2en: "Translation (fa2en)"
+  translation-ar2fa_ar2fa: "Translation (ar2fa)"
+  translation-fa2ar_fa2ar: "Translation (fa2ar)"
+  summarization_SamSUM-fa: "SamSum-Fa (Summarizaion)"
+  summarization_PnSummary: "PnSummary (Summarizaion)"
+  sentiment-analysis_deepsentipers: "DeepSentiPers (SA)"
+  sts_SynPerSTS: "SynPerSTS (STS)"
+  ner_arman: "Arman (NER)"
+  keyword-extraction_SynKeywords: "SynKeywords (Keyword Extraction)"
+  tone-classification_SynTone: "SynTone (Tone Classification)"
+  sts_FarSICK: "FarSICK (STS)"
+  paraphrase-detection_FarsiParaphraseDetection: "FarsiParaphraseDetection (Paraphrase Detection)"
+  nli_farstail: "Farstail (NLI)"
+  paraphrase-detection_parsinlu: "ParsiNLU (Paraphrase Detection)" # Assuming this was a typo or duplicate key and intended for update
+  extractive-qa_PQuAD: "PQuAD (Extractive QA)"
+  topic-classification_sid: "SID (Topic Classification)"
+
+
+
+# --- Model Display Configurations ---
+# Customize how model names are displayed and potentially override their URLs.
+# Keys here MUST be the canonical model identifiers found in the "Model Name" column
+# (or whatever column is specified by 'model_identifier_column' in global_settings)
+# of the .jsonl files generated by refresh.py.
+model_display_configs:
+  "claude-3-7-sonnet-20250219":
+    display_name: "Claude 3.7 Sonnet"
+    url: "https://www.anthropic.com/news/claude-3-7-sonnet"
+
+  "gpt-4.1":
+    display_name: "GPT-4.1"
+    url: "https://openai.com/index/gpt-4-1/"
+
+  "gpt-4o":
+    display_name: "GPT-4o"
+    url: "https://openai.com/index/hello-gpt-4o/"
+
+  "gpt-4.1-mini":
+    display_name: "GPT-4.1 Mini"
+    url: "https://openai.com/index/gpt-4-1/"
+
+  "deepseek-chat":
+    display_name: "DeepSeek-V3"
+    url: "https://api-docs.deepseek.com/"
+
+  "gemma-3-27b-it":
+    display_name: "Gemma 3 27B IT"
+    url: "https://huggingface.co/google/gemma-3-27b-it"
+
+  "gpt-4o-mini":
+    display_name: "GPT-4o Mini"
+    url: "https://openai.com/index/hello-gpt-4o/"
+
+  "Qwen3-32B":
+    display_name: "Qwen3-32B"
+    url: "https://huggingface.co/Qwen/Qwen3-32B"
+
+  "Llama-3.3-70B-Instruct":
+    display_name: "Llama 3.3 70B Instruct"
+    url: "https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct"
+
+  "gemma-3-12b-it":
+    display_name: "Gemma 3 12B IT"
+    url: "https://huggingface.co/google/gemma-3-12b-it"
+
+  "Qwen3-14B":
+    display_name: "Qwen3-14B"
+    url: "https://huggingface.co/Qwen/Qwen3-14B"
+
+  "Mistral-Small-3.1-24B-Instruct-2503":
+    display_name: "Mistral Small 3.1 24B Instruct"
+    url: "https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503"
+
+  "claude-3-5-haiku-20241022":
+    display_name: "Claude 3.5 Haiku"
+    url: "https://www.anthropic.com/claude/haiku"
+
+  "gpt-4.1-nano":
+    display_name: "GPT-4.1 Nano"
+    url: "https://openai.com/index/gpt-4-1/"
+
+  "Qwen3-8B":
+    display_name: "Qwen3-8B"
+    url: "https://huggingface.co/Qwen/Qwen3-8B"
+
+  "gemma-3-4b-it":
+    display_name: "Gemma 3 4B IT"
+    url: "https://huggingface.co/google/gemma-3-4b-it"
+
+  "aya-expanse-32b":
+    display_name: "Aya Expanse 32B"
+    url: "https://huggingface.co/CohereLabs/aya-expanse-32b"
+
+  "Qwen3-4B":
+    display_name: "Qwen3-4B"
+    url: "https://huggingface.co/Qwen/Qwen3-4B"
+
+  "gemma-3-1b-it":
+    display_name: "Gemma 3 1B IT"
+    url: "https://huggingface.co/google/gemma-3-1b-it"
+
+  "Mistral-7B-Instruct-v0.3":
+    display_name: "Mistral 7B Instruct v0.3"
+    url: "https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3"
+
+  "Llama-3.2-3B-Instruct":
+    display_name: "Llama 3.2 3B Instruct"
+    url: "https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct"
+
+  "Llama-3.2-1B-Instruct":
+    display_name: "Llama 3.2 1B Instruct"
+    url: "https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct"
+
+  "o4-mini":
+    display_name: "GPT-o4 Mini"
+    url: "https://openai.com/index/introducing-o3-and-o4-mini/"
+
+  "deepseek-reasoner":
+    display_name: "DeepSeek-R1"
+    url: "https://api-docs.deepseek.com/guides/reasoning_model"
+
+  "gemini-2.0-flash":
+    display_name: "Gemini 2.0 Flash"
+    url: "https://cloud.google.com/vertex-ai/generative-ai/docs/models/gemini/2-0-flash"
+
+  "gemini-2.5-flash-preview-05-20":
+    display_name: "Gemini 2.5 Flash Preview"
+    url: "https://cloud.google.com/vertex-ai/generative-ai/docs/models/gemini/2-5-flash"
+
+  "Qwen3-30B-A3B":
+    display_name: "Qwen3-30B-A3B"
+    url: "https://huggingface.co/Qwen/Qwen3-30B-A3B"
+
+  "c4ai-command-r-plus":
+    display_name: "Command R Plus"
+    url: "https://huggingface.co/CohereLabs/c4ai-command-r-plus"
+
+  "c4ai-command-r-v01":
+    display_name: "Command R v01"
+    url: "https://huggingface.co/CohereLabs/c4ai-command-r-v01"
+
+  "c4ai-command-a-03-2025":
+    display_name: "Command A"
+    url: "https://huggingface.co/CohereLabs/c4ai-command-a-03-2025"
+
+
+
+  # Add one entry for each model whose display name or URL you want to customize.
+  # If a model ID from your data is not listed here, its raw ID will be used as its name.
+
+# --- Global Settings ---
+# Various settings controlling leaderboard behavior and data interpretation.
+global_settings:
+  # The actual column name in your .jsonl DataFrames that holds the canonical model identifier.
+  # This identifier is used as the key to look up entries in 'model_display_configs'.
+  model_identifier_column: "Model Name"
+
+  # Defines the primary score column used for ranking within each task's table.
+  # Keys MUST match the keys in 'task_display_names' and 'tab_processing_order'.
+  # Values MUST be actual column names present in the corresponding .jsonl data files.
+  main_scores_map:
+    all: "Average"
+    mt_bench: "score_mean"
+    ifeval: "strict_instruction_accuracy"
+    MMLU: "acc"
+    persian_csr: "acc"
+    persian_nlg: "nlg_score"
+    persian_nlu: "nlu_score"
+    question-generation_PersianQA: "nlg_score"
+    translation-en2fa_en2fa: "nlg_score"
+    translation-fa2en_fa2en: "nlg_score"
+    translation-ar2fa_ar2fa: "nlg_score"
+    translation-fa2ar_fa2ar: "nlg_score"
+    summarization_SamSUM-fa: "nlg_score"
+    summarization_PnSummary: "nlg_score"
+
+    sentiment-analysis_deepsentipers: "nlu_score"
+    sts_SynPerSTS: "nlu_score"
+    ner_arman: "nlu_score"
+    keyword-extraction_SynKeywords: "nlu_score"
+    tone-classification_SynTone: "nlu_score"
+    sts_FarSICK: "nlu_score"
+    paraphrase-detection_FarsiParaphraseDetection: "nlu_score"
+    nli_farstail: "nlu_score"
+    paraphrase-detection_parsinlu: "nlu_score"
+    extractive-qa_PQuAD: "nlu_score"
+    topic-classification_sid: "nlu_score"
+
+
+
+  # Original column names in DataFrames that are allowed to have null values if an 'Average'
+  # score is calculated by leaderboard.py (though refresh.py now handles the 'all' table Average).
+  # Primarily non-score informational columns.
+  allowed_null_columns_in_average:
+    - "Model Name"
+    - "model_url"
+    - "parameters_count"
+    - "source_type"
+
+  # A score threshold, its specific use in formatting needs to be defined in leaderboard.py if used.
+  # For example, conditional formatting for scores above this value. (Currently not actively used in provided Python code for formatting).
+  score_cutoff_for_formatting: 0.0
+
+  # Defines the order of tabs in the UI and the order for loading data files by leaderboard.py.
+  # Keys MUST match:
+  #   1. Keys in 'task_display_names'.
+  #   2. Keys in 'main_scores_map'.
+  #   3. The base names of .jsonl files generated by refresh.py (e.g., "MMLU" for "MMLU.jsonl").
+  tab_processing_order:
+    - "all"
+    - "mt_bench"
+    - "ifeval"
+    - "MMLU"
+    - "persian_csr"
+    - "persian_nlg"
+    - "persian_nlu"
+  
+  numeric_score_columns_for_bolding: # List of ORIGINAL column names
+    # For the "Overall Benchmark" tab (all.jsonl)
+    - "Average"
+    - "Persian IFEval"
+    - "Persian MT-Bench"
+    - "PerMMLU"
+    - "PerCoR"
+    - "Persian NLU"
+    - "Persian NLG"
+    
+    # For individual task tabs (if you want to keep their main scores bolded there)
+    # These are typically the values from your 'main_scores_map'
+    - "score_mean"                  # For mt_bench tab
+    - "strict_instruction_accuracy" # For ifeval tab
+    - "acc"                         # For MMLU, persian_csr tabs
+    # "nlg_score" and "nlu_score" are already covered if "Persian NLG" 
+    # and "Persian NLU" are the actual column names in those specific tabs too.
+    # If persian_nlg.jsonl uses "nlg_score" as its main column, and 
+    # persian_nlu.jsonl uses "nlu_score", then you can add them for those specific tabs:
+    - "nlg_score"                   # For persian_nlg tab (if it's different from "Persian NLG")
+    - "nlu_score"                   # For persian_nlu tab (if it's different from "Persian NLU")
+    # Add any other specific metric columns from other .jsonl files 
+    # that you want to have their max value bolded in their respective tabs.
+
+  # Add this list:
+  columns_to_hide: # List of ORIGINAL column names you don't want to display
+    - "model_url"
+    - "source_type"
+    # - "another_column_to_hide" 
+
+  parent_child_task_map:
+    persian_nlg: # Parent task key
+      - "question-generation_PersianQA"
+      - "translation-en2fa_en2fa"
+      - "translation-fa2en_fa2en"
+      - "translation-ar2fa_ar2fa"
+      - "translation-fa2ar_fa2ar"
+      - "summarization_SamSUM-fa"
+      - "summarization_PnSummary"
+    persian_nlu: # Parent task key
+      - "sentiment-analysis_deepsentipers"
+      - "sts_SynPerSTS"
+      - "ner_arman"
+      - "keyword-extraction_SynKeywords"
+      - "tone-classification_SynTone"
+      - "sts_FarSICK"
+      - "paraphrase-detection_FarsiParaphraseDetection"
+      - "nli_farstail"
+      - "paraphrase-detection_parsinlu" # Assumin g this was a typo or duplicate key
+      - "extractive-qa_PQuAD"
+      - "topic-classification_sid"

leaderboard/refresh.py

@@ -0,0 +1,441 @@
+# leaderboard/refresh.py
+
+import json
+import logging
+from pathlib import Path
+from typing import Any, Dict, List, Optional
+import pandas as pd
+import yaml
+
+# --- Logging Setup ---
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s - %(levelname)s - %(module)s - %(message)s"
+)
+logger = logging.getLogger(__name__)
+
+# --- Path Definitions ---
+SCRIPT_DIR = Path(__file__).resolve().parent
+PROJECT_ROOT = SCRIPT_DIR.parent
+
+# --- Default Input/Output Paths ---
+DEFAULT_MODELS_FOLDER = PROJECT_ROOT.parent / "llm-leaderboard/models_info"
+DEFAULT_RESULTS_FOLDER = PROJECT_ROOT.parent / "llm-leaderboard/results"
+OUTPUT_FOLDER = SCRIPT_DIR / "boards_data"
+CONFIG_FILE_PATH = SCRIPT_DIR / "leaderboard_config.yaml"
+TEMPLATE_FOLDER = SCRIPT_DIR / "template_jsons"
+
+# --- Constants for Subtask Processing ---
+NLU_NLG_TASK_KEYS = ["persian_nlu", "persian_nlg"]
+
+ALL_LEADERBOARD_COLUMNS = [
+    'Model Name', 'model_url', 'parameters_count', 'source_type', 'Average',
+    'Persian IFEval', 'Persian MT-Bench', "PerMMLU",
+    "PerCoR", "Persian NLU", "Persian NLG"
+]
+
+
+def load_tasks_from_config(config_path: Path) -> Dict[str, str]:
+    
+    if not config_path.exists():
+        logger.error(f"Configuration file not found: {config_path}. Cannot load tasks.")
+        return {}
+    try:
+        with open(config_path, 'r', encoding='utf-8') as f:
+            config_data = yaml.safe_load(f)
+        tasks_from_config = config_data.get('task_display_names', {})
+        if not isinstance(tasks_from_config, dict):
+            logger.error(f"'task_display_names' in {config_path} is not a dictionary.")
+            return {}
+        processed_tasks = {k: v for k, v in tasks_from_config.items() if str(k).lower() != 'all'}
+        if not processed_tasks:
+            logger.warning(f"No tasks in {config_path} under 'task_display_names' (excluding 'all').")
+        return processed_tasks
+    except Exception as e:
+        logger.error(f"Error loading config {config_path}: {e}")
+        return {}
+
+class ModelEvaluationProcessor:
+    def __init__(
+        self,
+        models_info_path: Path,
+        results_base_path: Path,
+        output_path: Path,
+        template_jsons_path: Path,
+    ) -> None:
+        
+        self.models_info_path = models_info_path
+        self.results_base_path = results_base_path
+        self.output_path = output_path
+        self.template_folder = template_jsons_path
+        self.output_path.mkdir(parents=True, exist_ok=True)
+
+        self.tasks_config = load_tasks_from_config(CONFIG_FILE_PATH)
+        if not self.tasks_config:
+            logger.error("Tasks config is empty. Processing might be affected.")
+
+        self.main_scores_map = {
+            "ifeval": "strict_instruction_accuracy",
+            "mt_bench": "score_mean",
+            "MMLU": "acc",
+            "persian_csr": "acc",
+            "persian_nlg": "nlg_score",
+            "persian_nlu": "nlu_score",
+        }
+    def _load_template(self, task_key: str) -> Dict[str, Any]:
+        
+        path = self.template_folder / f"{task_key}.json"
+        try:
+            return json.loads(path.read_text(encoding="utf-8"))
+        except FileNotFoundError:
+            logger.warning(f"Template file not found for task_key {task_key} at {path}. Using empty template.")
+            return {}
+        except Exception as e:
+            logger.error(f"Cannot load template for task_key {task_key} from {path}: {e}")
+            return {}
+
+    def _deep_override(self, base: Any, override: Any) -> Any:
+        if isinstance(base, dict) and isinstance(override, dict):
+            merged = {}
+            for k, v_base in base.items():
+                if k in override and override[k] is not None and override[k] != -1:
+                    merged[k] = self._deep_override(v_base, override[k])
+                else:
+                    merged[k] = v_base
+            # for k, v_override in override.items():
+            #      if k not in merged:
+            #         merged[k] = v_override
+            return merged
+        elif override is not None and override != -1:
+            return override
+        else:
+            return base
+
+
+    def _load_model_raw_results(self, model_folder_name: str, task_key: str) -> Dict[str, Any]:
+        
+        results_filename = f"{model_folder_name}___{task_key}.json"
+        results_file_path = self.results_base_path / results_filename
+
+        if results_file_path.exists():
+            try:
+                with open(results_file_path, 'r', encoding='utf-8') as f:
+                    data = json.load(f)
+                return data if isinstance(data, dict) else {}
+            except json.JSONDecodeError as e:
+                logger.error(f"JSONDecodeError for model '{model_folder_name}', task_key '{task_key}' from {results_file_path}: {e}")
+            except Exception as e:
+                logger.error(f"Error loading results for model '{model_folder_name}', task_key '{task_key}' from {results_file_path}: {e}")
+        else:
+            logger.warning(f"Results file not found for model '{model_folder_name}', task_key '{task_key}' at {results_file_path}")
+        return {}
+
+    def load_and_fill_task_results(self, model_folder_name: str, task_key: str) -> Dict[str, Any]:
+        
+        template = self._load_template(task_key)
+        raw_results = self._load_model_raw_results(model_folder_name, task_key)
+        return self._deep_override(template, raw_results)
+
+    def clean_previous_subtask_files(self) -> None:
+        
+        logger.info("Cleaning previous NLU/NLG subtask JSONL files...")
+        for task_key_prefix in NLU_NLG_TASK_KEYS:
+            for result_file in self.results_base_path.glob(f"*___{task_key_prefix}.json"):
+                try:
+                    task_data_content = result_file.read_text(encoding="utf-8")
+                    if not task_data_content.strip():
+                        logger.debug(f"Skipping empty result file for subtask cleaning: {result_file}")
+                        continue
+                    task_data = json.loads(task_data_content)
+
+                    main_score_for_this_task_prefix = self.main_scores_map.get(task_key_prefix)
+
+                    for subtask_name in task_data:
+                        if subtask_name == main_score_for_this_task_prefix:
+                            continue
+                        if isinstance(task_data.get(subtask_name), dict):
+                            subtask_output_path = self.output_path / f"{subtask_name}.jsonl"
+                            if subtask_output_path.exists():
+                                subtask_output_path.unlink()
+                                logger.info(f"Deleted previous subtask file: {subtask_output_path}")
+                except json.JSONDecodeError as e:
+                    logger.warning(f"Failed to decode JSON for subtask cleaning from {result_file}: {e}")
+                except Exception as e:
+                    logger.warning(f"Failed to inspect/delete subtask files based on {result_file}: {e}")
+
+    def _process_subtask_data(self, task_results: Dict[str, Any], base_model_info: Dict[str, Any], parent_task_main_score_key: Optional[str], parent_task_key_for_log: str) -> None:
+        
+        parent_task_main_score_value = task_results.get(parent_task_main_score_key) if parent_task_main_score_key else None
+
+        for subtask_name, subtask_scores_dict in task_results.items():
+            if subtask_name == parent_task_main_score_key:
+                continue
+            if not isinstance(subtask_scores_dict, dict):
+                logger.debug(f"Skipping entry '{subtask_name}' in '{parent_task_key_for_log}': not a dictionary of subtask scores.")
+                continue
+
+            row_data = base_model_info.copy()
+            row_data.update(subtask_scores_dict)
+
+            if parent_task_main_score_key:
+                row_data[parent_task_main_score_key] = parent_task_main_score_value
+
+            subtask_output_file = f"{subtask_name}.jsonl"
+            subtask_output_path = self.output_path / subtask_output_file
+
+            try:
+                current_entries = []
+                if subtask_output_path.exists():
+                    existing_df = pd.read_json(subtask_output_path, lines=True)
+                    if not existing_df.empty and 'Model Name' in existing_df.columns:
+                         current_entries = existing_df[existing_df['Model Name'] != row_data['Model Name']].to_dict(orient='records')
+
+                current_entries.append(row_data)
+                updated_df = pd.DataFrame(current_entries)
+                updated_df.to_json(subtask_output_path, orient="records", lines=True, force_ascii=False)
+                logger.debug(f"Updated subtask file: {subtask_output_path} for model {base_model_info.get('Model Name')}, parent task {parent_task_key_for_log}")
+            except Exception as e:
+                logger.error(f"Error updating subtask file {subtask_output_path} for parent {parent_task_key_for_log}: {e}")
+    def process_nlu_nlg_subtasks(self, model_details: Dict[str, Any], model_folder_name: str, canonical_model_name: str) -> None:
+        
+        common_subtask_model_info = {
+            "Model Name": canonical_model_name,
+            "model_url": model_details.get('model_url', model_details.get('link', model_details.get('homepage', 'https://google.com'))),
+            "parameters_count": str(model_details.get('n_parameters', "N/A")),
+            "source_type": "Closed-Source" # Default, will be refined
+        }
+        parameters_count_raw = model_details.get('n_parameters', None)
+        if parameters_count_raw is not None:
+            is_open_source_candidate = False
+            if isinstance(parameters_count_raw, (int, float)) and parameters_count_raw > 0:
+                is_open_source_candidate = True
+            elif isinstance(parameters_count_raw, str) and \
+                    str(parameters_count_raw).strip().lower() not in ["", "n/a", "unknown", "private", "confidential", "tbd", "null", "closed"]:
+                is_open_source_candidate = True
+            common_subtask_model_info["source_type"] = "Open-Source" if is_open_source_candidate else "Closed-Source"
+
+        for task_key_for_subtasks in NLU_NLG_TASK_KEYS:
+            if task_key_for_subtasks not in self.tasks_config:
+                logger.debug(f"Subtask processing for '{task_key_for_subtasks}' skipped: not in tasks_config.")
+                continue
+
+            logger.info(f"Processing subtasks for '{task_key_for_subtasks}' for model '{canonical_model_name}'...")
+            parent_task_full_results = self.load_and_fill_task_results(model_folder_name, task_key_for_subtasks)
+            main_score_key_for_parent_task = self.main_scores_map.get(task_key_for_subtasks)
+            if not main_score_key_for_parent_task:
+                logger.warning(f"No main score key in main_scores_map for parent task '{task_key_for_subtasks}'.")
+
+            self._process_subtask_data(
+                parent_task_full_results,
+                common_subtask_model_info,
+                main_score_key_for_parent_task,
+                task_key_for_subtasks
+            )
+    def process_models(self) -> Dict[str, pd.DataFrame]:
+        processed_task_data: Dict[str, List[Dict[str, Any]]] = {task_key: [] for task_key in self.tasks_config.keys()}
+        all_models_summary_data: List[Dict[str, Any]] = []
+
+        if not self.models_info_path.exists() or not self.models_info_path.is_dir():
+            logger.critical(f"Configured MODELS_FOLDER path does not exist or is not a directory: {self.models_info_path}")
+            empty_dfs = {key: pd.DataFrame() for key in self.tasks_config.keys()}
+            empty_dfs["all"] = pd.DataFrame()
+            return empty_dfs
+
+        model_info_files = list(self.models_info_path.glob("*.json"))
+        if not model_info_files:
+            logger.warning(f"No model info files (*.json) found in {self.models_info_path}. No models will be processed.")
+            empty_dfs = {key: pd.DataFrame() for key in self.tasks_config.keys()}
+            empty_dfs["all"] = pd.DataFrame()
+            return empty_dfs
+
+        for model_info_file in model_info_files:
+            model_folder_name = model_info_file.stem
+            try:
+                with open(model_info_file, 'r', encoding='utf-8') as f:
+                    model_details = json.load(f)
+
+                canonical_model_name = model_details.get('name_for_leaderboard',
+                                      model_details.get('model_hf_id',
+                                      model_details.get('name', model_folder_name)))
+                model_url = model_details.get('model_url', model_details.get('link', model_details.get('homepage', 'https_google.com')))
+                if not model_url: model_url = 'https_google.com'
+
+                parameters_count_raw = model_details.get('n_parameters', None)
+                parameters_count_display = str(parameters_count_raw) if parameters_count_raw is not None else "N/A"
+
+                source_type = "Closed-Source"
+                if parameters_count_raw is not None:
+                    is_open_source_candidate = False
+                    if isinstance(parameters_count_raw, (int, float)) and parameters_count_raw > 0:
+                        is_open_source_candidate = True
+                    elif isinstance(parameters_count_raw, str) and \
+                         str(parameters_count_raw).strip().lower() not in ["", "n/a", "unknown", "private", "confidential", "tbd", "null", "closed"]:
+                        is_open_source_candidate = True
+                    source_type = "Open-Source" if is_open_source_candidate else "Closed-Source"
+
+            except Exception as e:
+                logger.error(f"Error loading/parsing model info from {model_info_file}: {e}. Skipping '{model_folder_name}'.")
+                continue
+
+            logger.info(f"Processing model: {canonical_model_name} (source ID: {model_folder_name})")
+
+            current_model_scores_for_summary: Dict[str, Any] = {
+                "Model Name": canonical_model_name,
+                "model_url": model_url,
+                "parameters_count": parameters_count_display,
+                "source_type": source_type
+            }
+
+            for task_key, task_display_name in self.tasks_config.items():
+                task_specific_results = self.load_and_fill_task_results(model_folder_name, task_key)
+                main_score_metric_name = self.main_scores_map.get(task_key)
+                task_data_entry_for_specific_jsonl: Dict[str, Any] = {
+                    "Model Name": canonical_model_name,
+                    "model_url": model_url,
+                    "parameters_count": parameters_count_display,
+                    "source_type": source_type
+                }
+
+                if isinstance(task_specific_results, dict) and task_specific_results:
+                    for metric, value in task_specific_results.items():
+                        task_data_entry_for_specific_jsonl[metric] = value
+
+                    if main_score_metric_name and main_score_metric_name in task_specific_results:
+                        score_value = task_specific_results[main_score_metric_name]
+                        if task_key == "mt_bench" and score_value is not None:
+                             try:
+                                score_value = float(score_value) / 10.0
+                             except (ValueError, TypeError):
+                                logger.warning(f"Could not convert mt_bench score '{score_value}' to float for division for model {canonical_model_name}")
+                                score_value = pd.NA
+                        current_model_scores_for_summary[task_display_name] = score_value
+                    elif main_score_metric_name:
+                        logger.warning(f"Main score metric '{main_score_metric_name}' for task '{task_key}' (Display: {task_display_name}) not found for model '{canonical_model_name}'. Will be NA.")
+                        current_model_scores_for_summary[task_display_name] = pd.NA
+                        task_data_entry_for_specific_jsonl[main_score_metric_name] = pd.NA
+                else:
+                    logger.warning(f"No valid results data for model '{canonical_model_name}', task_key '{task_key}'. Scores will be NA.")
+                    if main_score_metric_name:
+                        task_data_entry_for_specific_jsonl[main_score_metric_name] = pd.NA
+                    current_model_scores_for_summary[task_display_name] = pd.NA
+
+                processed_task_data[task_key].append(task_data_entry_for_specific_jsonl)
+
+            all_models_summary_data.append(current_model_scores_for_summary)
+            self.process_nlu_nlg_subtasks(model_details, model_folder_name, canonical_model_name)
+
+        final_dataframes: Dict[str, pd.DataFrame] = {}
+        for task_key, data_list in processed_task_data.items():
+            df = pd.DataFrame(data_list) if data_list else pd.DataFrame()
+            main_score_col = self.main_scores_map.get(task_key)
+            if not df.empty and main_score_col and main_score_col in df.columns:
+                try:
+                    df[main_score_col] = pd.to_numeric(df[main_score_col], errors='coerce')
+                    # Sort by main score (NaNs will go last or first depending on na_position, default is last)
+                    df = df.sort_values(by=main_score_col, ascending=False, na_position='last')
+                except Exception as e:
+                    logger.warning(f"Could not sort dataframe for task {task_key} by score {main_score_col}: {e}")
+            final_dataframes[task_key] = df
+            if df.empty:
+                 logger.warning(f"No data processed for task '{task_key}'. Resulting DataFrame is empty.")
+
+        if all_models_summary_data:
+            all_df = pd.DataFrame(all_models_summary_data)
+            score_cols_for_average = []
+            for _, task_display_name_for_avg in self.tasks_config.items():
+                if task_display_name_for_avg in all_df.columns:
+                    numeric_col = pd.to_numeric(all_df[task_display_name_for_avg], errors='coerce')
+                    if numeric_col.notna().any(): # Check if there is at least one non-NA numeric value
+                        all_df[task_display_name_for_avg] = numeric_col
+                        score_cols_for_average.append(task_display_name_for_avg)
+                    else: # All values are NA or non-numeric
+                        all_df[task_display_name_for_avg] = pd.NA # Ensure column is NA if not usable
+                        logger.warning(f"Column '{task_display_name_for_avg}' for averaging in 'all' table is not numeric or all NaN. Excluding from average calculation and setting to NA.")
+            if score_cols_for_average:
+                try:
+                    # Calculate mean; it will be NaN if any constituent score for a row is NaN.
+                    all_df["Average"] = all_df[score_cols_for_average].mean(axis=1, skipna=False)
+                    # Round only non-NaN averages
+                    all_df.loc[all_df["Average"].notna(), "Average"] = all_df.loc[all_df["Average"].notna(), "Average"].round(4)
+                except Exception as e:
+                    logger.error(f"Error calculating 'Average' for 'all' table: {e}. Average column might be NA or incorrect.")
+                    all_df["Average"] = pd.NA # Fallback to NA
+            else:
+                logger.warning("No valid numeric score columns found to calculate 'Average' for 'all' table.")
+                all_df["Average"] = pd.NA # Assign pd.NA if no columns to average
+
+            # Sort 'all' table by Average (NaNs will be placed last by default with ascending=False)
+            if "Average" in all_df.columns: # Check if 'Average' column exists
+                 # NaNs are typically sorted to the end by default when ascending=False or na_position='last'
+                all_df = all_df.sort_values(by="Average", ascending=False, na_position='last')
+
+
+            existing_cols_in_order = [col for col in ALL_LEADERBOARD_COLUMNS if col in all_df.columns]
+            other_cols = [col for col in all_df.columns if col not in existing_cols_in_order]
+            all_df = all_df[existing_cols_in_order + other_cols]
+
+            final_dataframes["all"] = all_df
+        else:
+            final_dataframes["all"] = pd.DataFrame()
+            logger.warning("No summary data collected for the 'all' table.")
+
+        return final_dataframes
+
+    def save_dataframe_as_jsonl(self, df: pd.DataFrame, filename_base: str) -> None:
+        
+        if df is None or df.empty:
+            logger.warning(f"DataFrame for '{filename_base}.jsonl' is empty or None. Skipping save.")
+            return
+        output_file_path = self.output_path / f"{filename_base}.jsonl"
+        try:
+            df.to_json(output_file_path, orient="records", lines=True, force_ascii=False, index=False)
+            logger.info(f"Saved data to {output_file_path}")
+        except Exception as e:
+            logger.error(f"Failed to save DataFrame to {output_file_path}: {e}")
+    def run(self) -> None:
+        
+        logger.info("Starting data processing pipeline in ModelEvaluationProcessor...")
+        self.clean_previous_subtask_files()
+        processed_dataframes = self.process_models()
+        for task_key_or_name, df in processed_dataframes.items():
+            self.save_dataframe_as_jsonl(df, task_key_or_name)
+        logger.info("Data processing pipeline completed successfully!")
+
+def main() -> None:
+    
+    models_folder_to_use = DEFAULT_MODELS_FOLDER
+    results_folder_to_use = DEFAULT_RESULTS_FOLDER
+    template_folder_to_use = TEMPLATE_FOLDER
+
+    logger.info(f"Refresh script running from: {SCRIPT_DIR}")
+    logger.info(f"CONFIGURED Input 'models_info' Path: {models_folder_to_use}")
+    logger.info(f"CONFIGURED Input 'results' Path: {results_folder_to_use}")
+    logger.info(f"CONFIGURED Input 'template_jsons' Path: {template_folder_to_use}")
+    logger.info(f"Outputting processed data to (inside 'leaderboard' dir): {OUTPUT_FOLDER}")
+    logger.info(f"Using configuration file (inside 'leaderboard' dir): {CONFIG_FILE_PATH}")
+
+    if not CONFIG_FILE_PATH.exists():
+        logger.critical(f"CRITICAL: Config file not found at {CONFIG_FILE_PATH}. Ensure '{CONFIG_FILE_PATH.name}' exists in '{SCRIPT_DIR}'.")
+        return
+    if not models_folder_to_use.exists() or not models_folder_to_use.is_dir():
+        logger.critical(f"CRITICAL: Input 'models_info' directory not found at {models_folder_to_use} or is not a directory.")
+        return
+    if not results_folder_to_use.exists() or not results_folder_to_use.is_dir():
+        logger.critical(f"CRITICAL: Input 'results' directory not found at {results_folder_to_use} or is not a directory.")
+        return
+    if not template_folder_to_use.exists() or not template_folder_to_use.is_dir():
+        logger.warning(f"WARNING: 'template_jsons' directory not found at {template_folder_to_use}. Template filling might not work as expected.")
+
+    try:
+        processor = ModelEvaluationProcessor(
+            models_info_path=models_folder_to_use,
+            results_base_path=results_folder_to_use,
+            output_path=OUTPUT_FOLDER,
+            template_jsons_path=template_folder_to_use,
+        )
+        processor.run()
+    except Exception as e:
+        logger.error(f"Unhandled exception in main: {e}", exc_info=True)
+
+if __name__ == "__main__":
+    main()

leaderboard/template_jsons/MMLU.json

@@ -0,0 +1,21 @@
+{
+  "acc": null,
+  "cinema_acc": null,
+  "emergency_number_acc": null,
+  "foods_acc": null,
+  "games_acc": null,
+  "herbal_drugs_acc": null,
+  "places_acc": null,
+  "poetry_acc": null,
+  "politicians_acc": null,
+  "popular_people_acc": null,
+  "Government_law_acc": null,
+  "proverbs_acc": null,
+  "religous_acc": null,
+  "social_manners_acc": null,
+  "souvenirs_acc": null,
+  "sports_acc": null,
+  "GPK_acc": null,
+  "SPK_acc": null,
+  "UPK_acc": null
+}

leaderboard/template_jsons/MMLU_full.json

@@ -0,0 +1,171 @@
+{
+  "acc": null,
+  "cinema_acc": null,
+  "emergency_number_acc": null,
+  "foods_acc": null,
+  "games_acc": null,
+  "herbal_drugs_acc": null,
+  "places_acc": null,
+  "poetry_acc": null,
+  "politicians_acc": null,
+  "popular_people_acc": null,
+  "Government_law_acc": null,
+  "proverbs_acc": null,
+  "religous_acc": null,
+  "social_manners_acc": null,
+  "souvenirs_acc": null,
+  "sports_acc": null,
+  "/class4/math4/_acc": null,
+  "/class4/farsi4/_acc": null,
+  "/class4/science4/_acc": null,
+  "/class4/religion4/_acc": null,
+  "/class4/social4/_acc": null,
+  "/class5/math5/_acc": null,
+  "/class5/farsi5/_acc": null,
+  "/class5/science5/_acc": null,
+  "/class5/religion5/_acc": null,
+  "/class5/social5/_acc": null,
+  "/class6/math6/_acc": null,
+  "/class6/farsi6/_acc": null,
+  "/class6/quran6/_acc": null,
+  "/class6/reigion6/_acc": null,
+  "/class6/social6/_acc": null,
+  "/class6/thinking6/_acc": null,
+  "/class6/science6/_acc": null,
+  "/class7/mathematic7/_acc": null,
+  "/class7/olum7/_acc": null,
+  "/class7/social7/_acc": null,
+  "/class7/farsi7/_acc": null,
+  "/class7/payam_asemani7/_acc": null,
+  "/class7/English7/_acc": null,
+  "/class7/quran7/_acc": null,
+  "/class8/mathematic8/_acc": null,
+  "/class8/farsi8/_acc": null,
+  "/class8/olum8/_acc": null,
+  "/class8/holy_message8/_acc": null,
+  "/class8/social8/_acc": null,
+  "/class8/englishstudent8/_acc": null,
+  "/class9/allum9/_acc": null,
+  "/class9/math_1/_acc": null,
+  "/class9/farsi9/_acc": null,
+  "/class9/social9/_acc": null,
+  "/class9/hollymessage9/_acc": null,
+  "/class9/englishstu9/_acc": null,
+  "/class10/geometric10/_acc": null,
+  "/class10/math10/_acc": null,
+  "/class10/physics10/_acc": null,
+  "/class10/chemistry10/_acc": null,
+  "/class10/biology10/_acc": null,
+  "/class10/farsi10/_acc": null,
+  "/class10/arabic10/_acc": null,
+  "/class10/religion_Life10/_acc": null,
+  "/class10/economy10/_acc": null,
+  "/class10/socialist10/_acc": null,
+  "/class10/historic10/_acc": null,
+  "/class10/arabic_ensani10/_acc": null,
+  "/class10/logic10/_acc": null,
+  "/class10/englishStu/_acc": null,
+  "/class10/joghraphy10/_acc": null,
+  "/class10/riazi10fani/_acc": null,
+  "/class10/physicfani/_acc": null,
+  "/class-11/relegion11/_acc": null,
+  "/class-11/arabic11/_acc": null,
+  "/class-11/frasi11/_acc": null,
+  "/class-11/english11/_acc": null,
+  "/class-11/tarikh11/_acc": null,
+  "/class-11/hesaban11/_acc": null,
+  "/class-11/hendese11/_acc": null,
+  "/class-11/static11/_acc": null,
+  "/class-11/physic11/_acc": null,
+  "/class-11/chemistry11/_acc": null,
+  "/class-11/mathematic11/_acc": null,
+  "/class-11/biology 11/_acc": null,
+  "/class-11/history11/_acc": null,
+  "/class-11/jeographic/_acc": null,
+  "/class-11/pholosophy/_acc": null,
+  "/class-11/englishworkbook11/_acc": null,
+  "/class-11/earabic11/_acc": null,
+  "/class-12/religion12/_acc": null,
+  "/class-12/mathematic/_acc": null,
+  "/class-12/englishstudentbook/_acc": null,
+  "/class-12/biology/_acc": null,
+  "/class-12/chemistry/_acc": null,
+  "/class-12/arabic/_acc": null,
+  "/class-12/farsi/_acc": null,
+  "/class-12/calculus12/_acc": null,
+  "/class-12/physic12/_acc": null,
+  "/class-12/geometric/_acc": null,
+  "/class-12/history12/_acc": null,
+  "/class-12/geographic12/_acc": null,
+  "/class-12/arabic12e/_acc": null,
+  "/class-12/philosophy12/_acc": null,
+  "/class-12/sociology12/_acc": null,
+  "/class-12/riazi12fani/_acc": null,
+  "dandoon 1402_acc": null,
+  "pezeshki 1400_acc": null,
+  "dandoon 1401_acc": null,
+  "darusazi 1401_acc": null,
+  "dandoon 1403_acc": null,
+  "dampezeshki 1395_acc": null,
+  "pezeshki 1402_acc": null,
+  "dampezeshki 1396_acc": null,
+  "ergonomi 1403_acc": null,
+  "darusazi 1403_acc": null,
+  "dampezeshki 1401_acc": null,
+  "ergonomi 1402_acc": null,
+  "pezeshki 1403_acc": null,
+  "darusazi 1400_acc": null,
+  "dandoon 1400_acc": null,
+  "darusazi 1402_acc": null,
+  "pezeshki 1401_acc": null,
+  "dampezeshki 1402_acc": null,
+  "bastan_404_acc": null,
+  "cinema_404_acc": null,
+  "cinema_402_acc": null,
+  "eghtesad_404_acc": null,
+  "ejtemai_404_acc": null,
+  "elahiat_404_acc": null,
+  "falsafe_404_acc": null,
+  "falsafe_402_acc": null,
+  "farsi_404_acc": null,
+  "farsi_402_acc": null,
+  "hoghoogh_404_acc": null,
+  "hoghoogh_402_acc": null,
+  "modiriat_404_acc": null,
+  "ravan_404_acc": null,
+  "sanaye_404_acc": null,
+  "siasi_404_acc": null,
+  "shimi 1402_acc": null,
+  "cs 1403_acc": null,
+  "bargh 1402_acc": null,
+  "industrial 1402_acc": null,
+  "metalogy 1404_acc": null,
+  "industrial 1403_acc": null,
+  "naft 1403_acc": null,
+  "omran 1402_acc": null,
+  "mechanic 1402_acc": null,
+  "shimi 1403_acc": null,
+  "cs 1402_acc": null,
+  "shimi 1404_acc": null,
+  "metalogy 1402_acc": null,
+  "naft 1402_acc": null,
+  "mechanic 1404_acc": null,
+  "riazi 1404_acc": null,
+  "cs 1404_acc": null,
+  "mechanic 1403_acc": null,
+  "ce 1404_acc": null,
+  "naft 1404_acc": null,
+  "riazi 1402_acc": null,
+  "bargh 1403_acc": null,
+  "industrial 1404_acc": null,
+  "omran 1403_acc": null,
+  "metalogy 1403_acc": null,
+  "bargh 1404_acc": null,
+  "riazi 1403_acc": null,
+  "ce 1403_acc": null,
+  "ce 1402_acc": null,
+  "omran 1404_acc": null,
+  "GPK_acc": null,
+  "SPK_acc": null,
+  "UPK_acc": null
+}

leaderboard/template_jsons/boolq.json

@@ -0,0 +1 @@
+{"boolq": null}

leaderboard/template_jsons/hamrah_mt_bench.json

@@ -0,0 +1,8 @@
+{
+  "s1": null,
+  "s2": null,
+  "s3": null,
+  "s4": null,
+  "score_w_mean": null,
+  "score_mean": null
+}

leaderboard/template_jsons/ifeval.json

@@ -0,0 +1,22 @@
+{
+  "strict_prompt_accuracy": null,
+  "strict_instruction_accuracy": null,
+  "loose_prompt_accuracy": null,
+  "loose_instruction_accuracy": null,
+  "strict_combination_category": null,
+  "strict_detectable_content_category": null,
+  "strict_detectable_format_category": null,
+  "strict_keywords_category": null,
+  "strict_language_category": null,
+  "strict_length_constraints_category": null,
+  "strict_punctuation_category": null,
+  "strict_startend_category": null,
+  "loose_combination_category": null,
+  "loose_detectable_content_category": null,
+  "loose_detectable_format_category": null,
+  "loose_keywords_category": null,
+  "loose_language_category": null,
+  "loose_length_constraints_category": null,
+  "loose_punctuation_category": null,
+  "loose_startend_category": null
+}

leaderboard/template_jsons/ifeval_full.json

@@ -0,0 +1,66 @@
+{
+  "strict_prompt_accuracy": null,
+  "strict_instruction_accuracy": null,
+  "loose_prompt_accuracy": null,
+  "loose_instruction_accuracy": null,
+  "strict_combination_category": null,
+  "strict_detectable_content_category": null,
+  "strict_detectable_format_category": null,
+  "strict_keywords_category": null,
+  "strict_language_category": null,
+  "strict_length_constraints_category": null,
+  "strict_punctuation_category": null,
+  "strict_startend_category": null,
+  "strict_combination:repeat_prompt_instruction": null,
+  "strict_combination:two_responses_instruction": null,
+  "strict_detectable_content:number_placeholders_instruction": null,
+  "strict_detectable_content:postscript_instruction": null,
+  "strict_detectable_format:constrained_response_instruction": null,
+  "strict_detectable_format:json_format_instruction": null,
+  "strict_detectable_format:multiple_sections_instruction": null,
+  "strict_detectable_format:number_bullet_lists_instruction": null,
+  "strict_detectable_format:number_highlighted_sections_instruction": null,
+  "strict_detectable_format:title_instruction": null,
+  "strict_keywords:existence_instruction": null,
+  "strict_keywords:forbidden_words_instruction": null,
+  "strict_keywords:frequency_instruction": null,
+  "strict_keywords:letter_frequency_instruction": null,
+  "strict_language:response_language_instruction": null,
+  "strict_length_constraints:nth_paragraph_first_word_instruction": null,
+  "strict_length_constraints:number_paragraphs_instruction": null,
+  "strict_length_constraints:number_sentences_instruction": null,
+  "strict_length_constraints:number_words_instruction": null,
+  "strict_punctuation:no_comma_instruction": null,
+  "strict_startend:end_checker_instruction": null,
+  "strict_startend:quotation_instruction": null,
+  "loose_combination_category": null,
+  "loose_detectable_content_category": null,
+  "loose_detectable_format_category": null,
+  "loose_keywords_category": null,
+  "loose_language_category": null,
+  "loose_length_constraints_category": null,
+  "loose_punctuation_category": null,
+  "loose_startend_category": null,
+  "loose_combination:repeat_prompt_instruction": null,
+  "loose_combination:two_responses_instruction": null,
+  "loose_detectable_content:number_placeholders_instruction": null,
+  "loose_detectable_content:postscript_instruction": null,
+  "loose_detectable_format:constrained_response_instruction": null,
+  "loose_detectable_format:json_format_instruction": null,
+  "loose_detectable_format:multiple_sections_instruction": null,
+  "loose_detectable_format:number_bullet_lists_instruction": null,
+  "loose_detectable_format:number_highlighted_sections_instruction": null,
+  "loose_detectable_format:title_instruction": null,
+  "loose_keywords:existence_instruction": null,
+  "loose_keywords:forbidden_words_instruction": null,
+  "loose_keywords:frequency_instruction": null,
+  "loose_keywords:letter_frequency_instruction": null,
+  "loose_language:response_language_instruction": null,
+  "loose_length_constraints:nth_paragraph_first_word_instruction": null,
+  "loose_length_constraints:number_paragraphs_instruction": null,
+  "loose_length_constraints:number_sentences_instruction": null,
+  "loose_length_constraints:number_words_instruction": null,
+  "loose_punctuation:no_comma_instruction": null,
+  "loose_startend:end_checker_instruction": null,
+  "loose_startend:quotation_instruction": null
+}

leaderboard/template_jsons/mt_bench.json

@@ -0,0 +1,24 @@
+{
+  "score_w_mean": null,
+  "score_mean": null,
+  "writing_score_w_mean": null,
+  "writing_score_mean": null,
+  "roleplay_score_w_mean": null,
+  "roleplay_score_mean": null,
+  "reasoning_score_w_mean": null,
+  "reasoning_score_mean": null,
+  "math_score_w_mean": null,
+  "math_score_mean": null,
+  "coding_score_w_mean": null,
+  "coding_score_mean": null,
+  "extraction_score_w_mean": null,
+  "extraction_score_mean": null,
+  "stem_score_w_mean": null,
+  "stem_score_mean": null,
+  "humanities_score_w_mean": null,
+  "humanities_score_mean": null,
+  "persian_general_knowledge_score_w_mean": null,
+  "persian_general_knowledge_score_mean": null,
+  "chatbot_rag_score_w_mean": null,
+  "chatbot_rag_score_mean": null
+}

leaderboard/template_jsons/mt_bench_full.json

@@ -0,0 +1,60 @@
+{
+  "s1": null,
+  "s2": null,
+  "s3": null,
+  "s4": null,
+  "score_w_mean": null,
+  "score_mean": null,
+  // "writing_s1": null,
+  // "writing_s2": null,
+  // "writing_s3": null,
+  // "writing_s4": null,
+  "writing_score_w_mean": null,
+  "writing_score_mean": null,
+  // "roleplay_s1": null,
+  // "roleplay_s2": null,
+  // "roleplay_s3": null,
+  // "roleplay_s4": null,
+  "roleplay_score_w_mean": null,
+  "roleplay_score_mean": null,
+  // "reasoning_s1": null,
+  // "reasoning_s2": null,
+  // "reasoning_s3": null,
+  // "reasoning_s4": null,
+  "reasoning_score_w_mean": null,
+  "reasoning_score_mean": null,
+  // "math_s1": null,
+  // "math_s2": null,
+  // "math_s3": null,
+  // "math_s4": null,
+  "math_score_w_mean": null,
+  "math_score_mean": null,
+  // "coding_s1": null,
+  // "coding_s2": null,
+  "coding_score_w_mean": null,
+  "coding_score_mean": null,
+  // "extraction_s1": null,
+  // "extraction_s2": null,
+  "extraction_score_w_mean": null,
+  "extraction_score_mean": null,
+  // "stem_s1": null,
+  // "stem_s2": null,
+  "stem_score_w_mean": null,
+  "stem_score_mean": null,
+  // "humanities_s1": null,
+  // "humanities_s2": null,
+  "humanities_score_w_mean": null,
+  "humanities_score_mean": null,
+  // "persian_general_knowledge_s1": null,
+  // "persian_general_knowledge_s2": null,
+  // "persian_general_knowledge_s3": null,
+  // "persian_general_knowledge_s4": null,
+  "persian_general_knowledge_score_w_mean": null,
+  "persian_general_knowledge_score_mean": null,
+  // "chatbot_rag_s1": null,
+  // "chatbot_rag_s2": null,
+  // "chatbot_rag_s3": null,
+  // "chatbot_rag_s4": null,
+  "chatbot_rag_score_w_mean": null,
+  "chatbot_rag_score_mean": null
+}

leaderboard/template_jsons/persian_csr.json

@@ -0,0 +1,47 @@
+{
+  "acc": null,
+  "acc_strict": null,
+  "donyaeeqtesad_acc": null,
+  "isna_acc": null,
+  "ninisite_article_acc": null,
+  "virgool_4_acc": null,
+  "khabaronline_acc": null,
+  "digiato_acc": null,
+  "doctoreto_acc": null,
+  "sarzamindownload_acc": null,
+  "hamgardi_acc": null,
+  "bigbangpage_acc": null,
+  "wiki_ahlolbait_acc": null,
+  "virgool_3_acc": null,
+  "virgool_2_acc": null,
+  "virgool_1_acc": null,
+  "hamshahrionline_acc": null,
+  "tabnak_acc": null,
+  "alibaba_acc": null,
+  "digikala_mag_acc": null,
+  "yjc_acc": null,
+  "beytoote_acc": null,
+  "asriran_acc": null,
+  "ecoiran_acc": null,
+  "hawzah_acc": null,
+  "zoomit_acc": null,
+  "wikipedia_acc": null,
+  "namnak_acc": null,
+  "khodro45_acc": null,
+  "fidibo_acc": null,
+  "newmiind_acc": null,
+  "taaghche_acc": null,
+  "motamem_acc": null,
+  "varzesh3_acc": null,
+  "mehrnews_acc": null,
+  "tasnim_acc": null,
+  "magerta_acc": null,
+  "radiokodak_book_acc": null,
+  "vipofilm_acc": null,
+  "wikishia_acc": null,
+  "voolak_acc": null,
+  "farsroid_acc": null,
+  "parsiday_acc": null,
+  "soft98_acc": null,
+  "ninisite_discussion_acc": null
+}

leaderboard/template_jsons/persian_csr_full.json

@@ -0,0 +1,117 @@
+{
+  "acc": null,
+  "acc_strict": null,
+  // "اما_acc": null,
+  // "از طرفی_acc": null,
+  // "چون_acc": null,
+  // "چراکه_acc": null,
+  // "به عنوان مثال_acc": null,
+  // "همچنین_acc": null,
+  // "سپس_acc": null,
+  // "تا_acc": null,
+  // "زیرا_acc": null,
+  // "بنابراین_acc": null,
+  // "چرا که_acc": null,
+  // "نیز_acc": null,
+  // "اگرچه_acc": null,
+  // "مثلا_acc": null,
+  // "هم‌چنین_acc": null,
+  // "همانطور که_acc": null,
+  // "برای مثال_acc": null,
+  // "با این وجود_acc": null,
+  // "یعنی_acc": null,
+  // "البته_acc": null,
+  // "کاش_acc": null,
+  // "ولی_acc": null,
+  // "لذا_acc": null,
+  // "علاوه بر آن_acc": null,
+  // "علاوه بر این_acc": null,
+  // "به همین دلیل_acc": null,
+  // "در حالی که_acc": null,
+  // "با این حال_acc": null,
+  // "حتی_acc": null,
+  // "از سوی دیگر_acc": null,
+  // "از طرف دیگر_acc": null,
+  // "که_acc": null,
+  // "به طور مثال_acc": null,
+  // "بلکه_acc": null,
+  // "برای نمونه_acc": null,
+  // "به همین علت_acc": null,
+  // "درحالی‌که_acc": null,
+  // "ناگهان_acc": null,
+  // "به عنوان نمونه_acc": null,
+  // "در حالی‌که_acc": null,
+  // "با این همه_acc": null,
+  "donyaeeqtesad_acc": null,
+  "isna_acc": null,
+  "ninisite_article_acc": null,
+  "virgool_4_acc": null,
+  "khabaronline_acc": null,
+  "digiato_acc": null,
+  "doctoreto_acc": null,
+  "sarzamindownload_acc": null,
+  "hamgardi_acc": null,
+  "bigbangpage_acc": null,
+  "wiki_ahlolbait_acc": null,
+  "virgool_3_acc": null,
+  "virgool_2_acc": null,
+  "virgool_1_acc": null,
+  "hamshahrionline_acc": null,
+  "tabnak_acc": null,
+  "alibaba_acc": null,
+  "digikala_mag_acc": null,
+  "yjc_acc": null,
+  "beytoote_acc": null,
+  "asriran_acc": null,
+  "ecoiran_acc": null,
+  "hawzah_acc": null,
+  "zoomit_acc": null,
+  "wikipedia_acc": null,
+  "namnak_acc": null,
+  "khodro45_acc": null,
+  "fidibo_acc": null,
+  "newmiind_acc": null,
+  "taaghche_acc": null,
+  "motamem_acc": null,
+  "varzesh3_acc": null,
+  "mehrnews_acc": null,
+  "tasnim_acc": null,
+  "magerta_acc": null,
+  "radiokodak_book_acc": null,
+  "vipofilm_acc": null,
+  "wikishia_acc": null,
+  "voolak_acc": null,
+  "farsroid_acc": null,
+  "parsiday_acc": null,
+  "soft98_acc": null,
+  "ninisite_discussion_acc": null,
+  // "اما_acc_strict": null,
+  // "از طرفی_acc_strict": null,
+  // "چون_acc_strict": null,
+  // "چراکه_acc_strict": null,
+  // "به عنوان مثال_acc_strict": null,
+  // "همچنین_acc_strict": null,
+  // "سپس_acc_strict": null,
+  // "تا_acc_strict": null,
+  // "زیرا_acc_strict": null,
+  // "بنابراین_acc_strict": null,
+  // "چرا که_acc_strict": null,
+  // "نیز_acc_strict": null,
+  // "اگرچه_acc_strict": null,
+  // "مثلا_acc_strict": null,
+  // "هم‌چنین_acc_strict": null,
+  // "همانطور که_acc_strict": null,
+  // "برای مثال_acc_strict": null,
+  // "با این وجود_acc_strict": null,
+  // "حتی_acc_strict": null,
+  // "از سوی دیگر_acc_strict": null,
+  // "از طرف دیگر_acc_strict": null,
+  // "که_acc_strict": null,
+  // "به طور مثال_acc_strict": null,
+  // "بلکه_acc_strict": null,
+  // "برای نمونه_acc_strict": null,
+  // "به همین علت_acc_strict": null,
+  // "درحالی‌که_acc_strict": null,
+  // "ناگهان_acc_strict": null,
+  // "با این همه_acc_strict": null
+}

leaderboard/template_jsons/persian_nlg.json

@@ -0,0 +1,48 @@
+{
+  "question-generation_PersianQA": {
+    "question-generation_PersianQA_rougeL_precision": null,
+    "question-generation_PersianQA_rougeL_recall": null,
+    "question-generation_PersianQA_rougeL_f1_score": null
+  },
+  "translation-en2fa_en2fa": {
+    "translation-en2fa_en2fa_bleu": null,
+    "translation-en2fa_en2fa_epoque_bleu": null,
+    "translation-en2fa_en2fa_mizan_bleu": null,
+    "translation-en2fa_en2fa_quran_bleu": null,
+    "translation-en2fa_en2fa_sahife_bleu": null,
+    "translation-en2fa_en2fa_nahj_bleu": null,
+    "translation-en2fa_en2fa_tep_bleu": null
+  },
+  "summarization_SamSUM-fa": {
+    "summarization_SamSUM-fa_rougeL_precision": null,
+    "summarization_SamSUM-fa_rougeL_recall": null,
+    "summarization_SamSUM-fa_rougeL_f1_score": null
+  },
+  "translation-fa2en_fa2en": {
+    "translation-fa2en_fa2en_bleu": null,
+    "translation-fa2en_fa2en_tep_bleu": null,
+    "translation-fa2en_fa2en_mizan_bleu": null,
+    "translation-fa2en_fa2en_quran_bleu": null,
+    "translation-fa2en_fa2en_epoque_bleu": null,
+    "translation-fa2en_fa2en_nahj_bleu": null,
+    "translation-fa2en_fa2en_sahife_bleu": null
+  },
+  "translation-ar2fa_ar2fa": {
+    "translation-ar2fa_ar2fa_bleu": null,
+    "translation-ar2fa_ar2fa_sahife_bleu": null,
+    "translation-ar2fa_ar2fa_nahj_bleu": null,
+    "translation-ar2fa_ar2fa_quran_bleu": null
+  },
+  "summarization_PnSummary": {
+    "summarization_PnSummary_rougeL_precision": null,
+    "summarization_PnSummary_rougeL_recall": null,
+    "summarization_PnSummary_rougeL_f1_score": null
+  },
+  "translation-fa2ar_fa2ar": {
+    "translation-fa2ar_fa2ar_bleu": null,
+    "translation-fa2ar_fa2ar_nahj_bleu": null,
+    "translation-fa2ar_fa2ar_sahife_bleu": null,
+    "translation-fa2ar_fa2ar_quran_bleu": null
+  },
+  "nlg_score": null
+}

leaderboard/template_jsons/persian_nlu.json

@@ -0,0 +1,93 @@
+{
+  "sentiment-analysis_deepsentipers": {
+    "sentiment-analysis_deepsentipers_acc_modified": null,
+    "sentiment-analysis_deepsentipers_precision_modified": null,
+    "sentiment-analysis_deepsentipers_recall_modified": null,
+    "sentiment-analysis_deepsentipers_fscore_modified": null,
+    "sentiment-analysis_deepsentipers_acc": null,
+    "sentiment-analysis_deepsentipers_precision": null,
+    "sentiment-analysis_deepsentipers_recall": null,
+    "sentiment-analysis_deepsentipers_fscore": null,
+    "sentiment-analysis_deepsentipers_valid_output_ratio": null
+  },
+  "sts_SynPerSTS": {
+    "sts_SynPerSTS_corrcoef_modified": null,
+    "sts_SynPerSTS_corrcoef": null,
+    "sts_SynPerSTS_valid_output_ratio": null
+  },
+  "ner_arman": {
+    "ner_arman_f1_mean": null,
+    "ner_arman_precision_mean": null,
+    "ner_arman_recall_mean": null
+  },
+  "keyword-extraction_SynKeywords": {
+    "keyword-extraction_SynKeywords_f1_mean": null,
+    "keyword-extraction_SynKeywords_precision_mean": null,
+    "keyword-extraction_SynKeywords_recall_mean": null
+  },
+  "tone-classification_SynTone": {
+    "tone-classification_SynTone_acc_modified": null,
+    "tone-classification_SynTone_precision_modified": null,
+    "tone-classification_SynTone_recall_modified": null,
+    "tone-classification_SynTone_fscore_modified": null,
+    "tone-classification_SynTone_acc": null,
+    "tone-classification_SynTone_precision": null,
+    "tone-classification_SynTone_recall": null,
+    "tone-classification_SynTone_fscore": null,
+    "tone-classification_SynTone_valid_output_ratio": null
+  },
+  "sts_FarSICK": {
+    "sts_FarSICK_corrcoef_modified": null,
+    "sts_FarSICK_corrcoef": null,
+    "sts_FarSICK_valid_output_ratio": null
+  },
+  "paraphrase-detection_FarsiParaphraseDetection": {
+    "paraphrase-detection_FarsiParaphraseDetection_acc_modified": null,
+    "paraphrase-detection_FarsiParaphraseDetection_precision_modified": null,
+    "paraphrase-detection_FarsiParaphraseDetection_recall_modified": null,
+    "paraphrase-detection_FarsiParaphraseDetection_fscore_modified": null,
+    "paraphrase-detection_FarsiParaphraseDetection_acc": null,
+    "paraphrase-detection_FarsiParaphraseDetection_precision": null,
+    "paraphrase-detection_FarsiParaphraseDetection_recall": null,
+    "paraphrase-detection_FarsiParaphraseDetection_fscore": null,
+    "paraphrase-detection_FarsiParaphraseDetection_valid_output_ratio": null
+  },
+  "nli_farstail": {
+    "nli_farstail_acc_modified": null,
+    "nli_farstail_precision_modified": null,
+    "nli_farstail_recall_modified": null,
+    "nli_farstail_fscore_modified": null,
+    "nli_farstail_acc": null,
+    "nli_farstail_precision": null,
+    "nli_farstail_recall": null,
+    "nli_farstail_fscore": null,
+    "nli_farstail_valid_output_ratio": null
+  },
+  "paraphrase-detection_parsinlu": {
+    "paraphrase-detection_parsinlu_acc_modified": null,
+    "paraphrase-detection_parsinlu_precision_modified": null,
+    "paraphrase-detection_parsinlu_recall_modified": null,
+    "paraphrase-detection_parsinlu_fscore_modified": null,
+    "paraphrase-detection_parsinlu_acc": null,
+    "paraphrase-detection_parsinlu_precision": null,
+    "paraphrase-detection_parsinlu_recall": null,
+    "paraphrase-detection_parsinlu_fscore": null,
+    "paraphrase-detection_parsinlu_valid_output_ratio": null
+  },
+  "extractive-qa_PQuAD": {
+    "extractive-qa_PQuAD_exact_match": null,
+    "extractive-qa_PQuAD_f1": null
+  },
+  "topic-classification_sid": {
+    "topic-classification_sid_acc_modified": null,
+    "topic-classification_sid_precision_modified": null,
+    "topic-classification_sid_recall_modified": null,
+    "topic-classification_sid_fscore_modified": null,
+    "topic-classification_sid_acc": null,
+    "topic-classification_sid_precision": null,
+    "topic-classification_sid_recall": null,
+    "topic-classification_sid_fscore": null,
+    "topic-classification_sid_valid_output_ratio": null
+  },
+  "nlu_score": null
+}

leaderboard/template_jsons/piqa.json

@@ -0,0 +1 @@
+{"piqa": null}

submission.py

@@ -0,0 +1,209 @@
+import gradio as gr
+import csv
+import os
+from datetime import datetime
+from pathlib import Path
+import pandas as pd
+import io # To handle string as a file-like object for pandas
+import logging
+
+from huggingface_hub import HfApi, HfFolder, hf_hub_download
+from huggingface_hub.utils import HfHubHTTPError, EntryNotFoundError # For specific error handling
+
+# --- Logging Setup ---
+# (Add this if not already present, or integrate with a central logging config)
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s - %(levelname)s - %(module)s - %(message)s"
+)
+logger = logging.getLogger(__name__)
+
+# --- Hugging Face Hub Configuration ---
+# IMPORTANT: Replace with your actual repository details
+TARGET_REPO_ID = "MCINext/submitted-models"  # e.g., "MehranS/MIZAN_submissions" # Suggested change for consistency
+TARGET_REPO_TYPE = "dataset"  # Recommended type for storing data
+FILENAME_IN_REPO = "model_submissions.csv"  # The name of the CSV file within the Hub repository
+
+# Define the header for your CSV file. This must be consistent.
+CSV_HEADER = [
+    'timestamp', 'model_name', 'base_model', 'revision',
+    'precision', 'weight_type', 'model_type', 'status', 'submission_type'
+]
+
+def get_hf_token() -> str | None:
+    """Retrieves the Hugging Face token from environment variables or HfFolder."""
+    token = os.environ.get("HF_TOKEN")  # Standard for Spaces secrets
+    if not token:
+        try:
+            token = HfFolder.get_token() # Fallback for local development after CLI login
+        except Exception:
+            logger.warning("Hugging Face token not found in HfFolder and HF_TOKEN env var is not set.")
+            token = None
+    return token
+
+def add_new_eval_hf_to_hub(model_name_hf_id: str, revision_hf: str) -> gr.Markdown:
+    """
+    Handles new Hugging Face model evaluation requests by saving them to a CSV file
+    in a specified Hugging Face Hub repository.
+    """
+    if not model_name_hf_id:
+        return gr.Markdown("⚠️ **Model Name (Hugging Face ID) is required.** Please enter a valid Hugging Face model ID.")
+
+    token = get_hf_token()
+    if not token:
+        error_html = "<div style='color:red; padding:10px; border:1px solid red; border-radius:5px;'>⚠️ **Configuration Error:** Hugging Face Token not found. Cannot save submission to the Hub. Please ensure the `HF_TOKEN` Space secret is set with write permissions to the target repository.</div>"
+        return gr.Markdown(error_html)
+
+    api = HfApi(token=token)
+    timestamp = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
+
+    submission_data = {
+        'timestamp': timestamp,
+        'model_name': model_name_hf_id.strip(),
+        'base_model': 'N/A', # As per the simple form's design
+        'revision': revision_hf.strip() if revision_hf else 'main',
+        'precision': 'To be fetched/determined',
+        'weight_type': 'To be fetched/determined',
+        'model_type': 'To be fetched/determined',
+        'status': 'pending_hub_submission', # New status indicating it's for Hub processing
+        'submission_type': 'huggingface_simple_form_to_hub' # New type
+    }
+
+    try:
+        # 1. Attempt to download the existing CSV from the Hub
+        try:
+            local_download_path = hf_hub_download(
+                repo_id=TARGET_REPO_ID,
+                filename=FILENAME_IN_REPO,
+                repo_type=TARGET_REPO_TYPE,
+                token=token,
+                # force_download=True, # Consider this if caching becomes an issue
+            )
+            # Read the downloaded CSV into a pandas DataFrame
+            df = pd.read_csv(local_download_path)
+            # Ensure columns match CSV_HEADER, add missing ones with NaN if necessary
+            for col in CSV_HEADER:
+                if col not in df.columns:
+                    df[col] = pd.NA
+            df = df[CSV_HEADER] # Reorder/select columns to match header
+            file_exists_on_hub = True
+            logger.info(f"Successfully downloaded existing '{FILENAME_IN_REPO}' from '{TARGET_REPO_ID}'.")
+        except EntryNotFoundError:
+            logger.info(f"'{FILENAME_IN_REPO}' not found in '{TARGET_REPO_ID}'. A new file will be created.")
+            df = pd.DataFrame(columns=CSV_HEADER) # Create an empty DataFrame with the correct headers
+            file_exists_on_hub = False
+        except HfHubHTTPError as e:
+            logger.error(f"HTTP error downloading '{FILENAME_IN_REPO}' from '{TARGET_REPO_ID}': {e.status_code} - {e.hf_raise}")
+            error_html = f"<div style='color:red; padding:10px; border:1px solid red; border-radius:5px;'>⚠️ **Hub Error:** Could not access the repository '{TARGET_REPO_ID}'. (HTTP {e.status_code}). Please check token permissions and repository ID.</div>"
+            return gr.Markdown(error_html)
+
+        # 2. Append the new submission data
+        new_row_df = pd.DataFrame([submission_data])
+        df = pd.concat([df, new_row_df], ignore_index=True)
+
+        # 3. Convert the DataFrame back to CSV in-memory
+        csv_buffer = io.StringIO()
+        df.to_csv(csv_buffer, index=False, header=True) # Always include header
+        csv_content_bytes = csv_buffer.getvalue().encode('utf-8')
+        csv_buffer.close()
+
+        # 4. Upload the updated CSV content to the Hub
+        commit_message = f"Add submission: {submission_data['model_name']} (rev: {submission_data['revision']})"
+        if not file_exists_on_hub:
+            commit_message = f"Create '{FILENAME_IN_REPO}' and add first submission: {submission_data['model_name']}"
+
+        api.upload_file(
+            path_or_fileobj=csv_content_bytes, # Pass the bytes directly
+            path_in_repo=FILENAME_IN_REPO,
+            repo_id=TARGET_REPO_ID,
+            repo_type=TARGET_REPO_TYPE,
+            commit_message=commit_message
+        )
+
+        logger.info(f"Submission for '{submission_data['model_name']}' pushed to '{TARGET_REPO_ID}/{FILENAME_IN_REPO}'.")
+        success_message_html = f"""
+        <div style='color:green; padding:10px; border:1px solid green; border-radius:5px;'>
+            ✅ Request for Hugging Face model '<strong>{submission_data['model_name']}</strong>' (Revision: {submission_data['revision']}) has been successfully submitted to the central repository on Hugging Face Hub!
+        </div>
+        """
+        return gr.Markdown(success_message_html)
+
+    except Exception as e:
+        logger.error(f"An unexpected error occurred while processing submission to Hugging Face Hub: {e}", exc_info=True)
+        error_html = f"<div style='color:red; padding:10px; border:1px solid red; border-radius:5px;'>⚠️ **System Error:** An unexpected error occurred: {e}. Please try again or contact support.</div>"
+        return gr.Markdown(error_html)
+
+
+def render_submit():
+    # Text for Introduction and Option 1 (Hugging Face Form)
+    intro_and_option1_guidance = """
+# Request Model Evaluation for MIZAN
+
+We're excited to evaluate new models for **MIZAN: A Persian LLM Leaderboard**!
+Please choose the submission path that best fits how your model can be accessed for evaluation.
+
+---
+
+### **Option 1: Your model is publicly available on Hugging Face Hub**
+
+If your model and its tokenizer can be loaded directly using their Hugging Face identifier (e.g., `username/model_name`), you can use the simplified form below to submit its key identifiers. Your submission will be added to our central tracking repository on the Hugging Face Hub. Our team will attempt to gather other necessary details from the Hub.
+"""
+
+    # Text for Option 2 (Email Submission)
+    option2_email_guidance = """
+---
+
+### **Option 2: Your model is NOT on Hugging Face, is private, or requires custom setup**
+
+If your model is hosted elsewhere, is private, requires specific access permissions, needs custom inference code, or involves a more complex setup for evaluation, please initiate your submission request via email.
+
+**To submit via email, please send comprehensive details to:**
+📧 **[email protected]**
+
+Our team will review your email and work with you to facilitate the evaluation process.
+    """
+
+    with gr.Blocks() as submit_tab_interface:
+        gr.Markdown(intro_and_option1_guidance)
+
+        with gr.Group():
+            gr.Markdown("### ✨ Form for Option 1: Submit a Hugging Face Model to the Hub")
+            
+            model_name_textbox_hf = gr.Textbox(
+                label="Model Name (Hugging Face ID: e.g., username/model_name)",
+                placeholder="bigscience/bloom-560m"
+            )
+            revision_name_textbox_hf = gr.Textbox(
+                label="Revision/Commit (Optional, defaults to 'main' if left empty)",
+                placeholder="e.g., main, or a specific commit hash"
+            )
+            
+            request_hf_button = gr.Button("🚀 Request Evaluation & Submit to Hub", variant="primary")
+        
+        submission_result_hf_form = gr.Markdown()
+
+        request_hf_button.click(
+            fn=add_new_eval_hf_to_hub, # Use the new function
+            inputs=[
+                model_name_textbox_hf,
+                revision_name_textbox_hf,
+            ],
+            outputs=submission_result_hf_form,
+        )
+        
+        gr.Markdown(option2_email_guidance)
+
+    return submit_tab_interface
+
+# For direct testing of this file:
+if __name__ == '__main__':
+    # You would need to set TARGET_REPO_ID and have a valid HF_TOKEN env var or be logged in.
+    # Example: os.environ["HF_TOKEN"] = "your_hf_write_token"
+    # TARGET_REPO_ID = "your-user/your-test-dataset" # Make sure this repo exists
+    
+    if not TARGET_REPO_ID.startswith("YOUR_"): # Basic check to prevent running with placeholder
+        print(f"Testing submission to Hub. Target repo: {TARGET_REPO_ID}")
+        test_interface = render_submit()
+        test_interface.launch(debug=True)
+    else:
+        print("Please update TARGET_REPO_ID in submission.py before running this test.")