Add my custom leaderboard files

.ipynb_checkpoints/README-checkpoint.md

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+---
+title: MediLingua Leaderboard
+emoji: 🚀
+colorFrom: blue
+colorTo: purple
+sdk: gradio
+sdk_version: 5.45.0
+app_file: app.py
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

Makefile

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+.PHONY: style format
+
+
+style:
+	python -m black --line-length 119 .
+	python -m isort .
+	ruff check --fix .
+
+
+quality:
+	python -m black --check --line-length 119 .
+	python -m isort --check-only .
+	ruff check .

app.py

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+import gradio as gr
+from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns
+import pandas as pd
+from apscheduler.schedulers.background import BackgroundScheduler
+from huggingface_hub import snapshot_download
+from src.about import CITATION_BUTTON_LABEL, CITATION_BUTTON_TEXT, EVALUATION_QUEUE_TEXT, INTRODUCTION_TEXT, LLM_BENCHMARKS_TEXT, TITLE
+from src.tasks import TASK_DESCRIPTIONS, MEASURE_DESCRIPTION
+from src.display.css_html_js import custom_css
+from src.display.utils import BENCHMARK_COLS, COLS, EVAL_COLS, EVAL_TYPES, AutoEvalColumn, ModelType, fields, WeightType, Precision
+from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, QUEUE_REPO, REPO_ID, RESULTS_REPO, TOKEN
+from src.populate import get_evaluation_queue_df, get_leaderboard_df
+from src.submission.submit import add_new_eval
+import random
+
+# Define task metadata (icons, names, descriptions)
+TASK_METADATA_MULTIPLECHOICE = {
+#    "TE": {"icon": "📊", "name": "Textual Entailment", "tooltip": ""},
+#    "SA": {"icon": "😃", "name": "Sentiment Analysis", "tooltip": ""},
+#    "HS": {"icon": "⚠️", "name": "Hate Speech", "tooltip": ""},
+#    "AT": {"icon": "🏥", "name": "Admission Test", "tooltip": ""},
+#    "WIC": {"icon": "🔤", "name": "Word in Context", "tooltip": ""},
+#    "FAQ": {"icon": "❓", "name": "Frequently Asked Questions", "tooltip": ""}
+}
+
+# Define task metadata (icons, names, descriptions)
+TASK_METADATA_GENERATIVE = {
+#    "LS": {"icon": "🔄", "name": "Lexical Substitution", "tooltip": ""},
+#    "SU": {"icon": "📝", "name": "Summarization", "tooltip": ""},
+    "NER": {"icon": "🏷️", "name": "Named Entity Recognition", "tooltip": ""},
+    "REL": {"icon": "🔗", "name": "Relation Extraction", "tooltip": ""},
+}
+
+def restart_space():
+    """Restart the Hugging Face space."""
+    API.restart_space(repo_id=REPO_ID)
+
+
+def init_leaderboard(dataframe, default_selection=None, hidden_columns=None):
+    """
+    Initialize and return the leaderboard when it is first loaded or when 'benchmark' is selected.
+    The table is sorted based on the "Avg. Combined Performance" field.
+    """
+    if dataframe is None or dataframe.empty:
+        raise ValueError("Leaderboard DataFrame is empty or None.")
+
+    field_list = fields(AutoEvalColumn)
+
+    return Leaderboard(
+        value=dataframe,
+        datatype=[c.type for c in field_list],
+        #select_columns=SelectColumns(
+        #    default_selection=default_selection or [c.name for c in field_list if c.displayed_by_default],
+        #    cant_deselect=[c.name for c in field_list if c.never_hidden],
+        #    label="Select Columns to Display:",
+        #),
+        search_columns=[AutoEvalColumn.model.name, AutoEvalColumn.license.name],
+        hide_columns=hidden_columns or [c.name for c in field_list if c.hidden],
+        filter_columns=[
+            ColumnFilter(AutoEvalColumn.fewshot_symbol.name, type="checkboxgroup", label="N-Few-Shot Learning (FS)"),
+            ColumnFilter(AutoEvalColumn.LANG.name, type="checkboxgroup", label="Languges "),
+        #   ColumnFilter(AutoEvalColumn.params.name, type="slider", min=0, max=150, label="Select the number of parameters (B)"),
+        ],
+        #filter_columns=[
+        #    ColumnFilter("IS_FS", type="checkbox", default=False, label="5-Few-Shot")
+        #    #ColumnFilter("FS", type="dropdown", label="5-Few-Shot")
+        #],
+        bool_checkboxgroup_label="Evaluation Mode",
+        interactive=False,
+    )
+
+def update_task_leaderboard(dataframe, default_selection=None, hidden_columns=None):
+    """
+    Update and return the leaderboard when a specific task is selected.
+    The table is sorted based on the "Combined Performance" field.
+    """
+    if dataframe is None or dataframe.empty:
+        raise ValueError("Leaderboard DataFrame is empty or None.")
+    print ("-----------")
+    print(dataframe)
+    print("columns : ", dataframe.columns)
+    print ("-----------")
+    
+    #sorted_dataframe = dataframe.sort_values(by="Combined Performance", ascending=False)
+    sorted_dataframe = dataframe.sort_values(by="Avg. Combined Performance ⬆️", ascending=False)
+ 
+    #print(sorted_dataframe['Combined Performance'])
+   
+    field_list = fields(AutoEvalColumn)
+
+    return Leaderboard(
+        value=sorted_dataframe,
+        datatype=[c.type for c in field_list],
+        #select_columns=SelectColumns(
+        #    default_selection=default_selection or [c.name for c in field_list if c.displayed_by_default],
+        #    cant_deselect=[c.name for c in field_list if c.never_hidden],
+        #    label="Select Columns to Display:",
+        #),
+        search_columns=[AutoEvalColumn.model.name, AutoEvalColumn.license.name],
+        hide_columns=hidden_columns or [c.name for c in field_list if c.hidden],
+        filter_columns=[
+            ColumnFilter(AutoEvalColumn.fewshot_symbol.name, type="checkboxgroup", label="N-Few-Shot Learning (FS)"),
+            ColumnFilter(AutoEvalColumn.LANG.name, type="checkboxgroup", label="Languges "),
+        ],
+        bool_checkboxgroup_label="Evaluation Mode",
+        interactive=False
+    )
+
+'''
+# Helper function for leaderboard initialization
+def init_leaderboard(dataframe, default_selection=None, hidden_columns=None):
+    """Initialize and return a leaderboard."""
+    if dataframe is None or dataframe.empty:
+        raise ValueError("Leaderboard DataFrame is empty or None.")
+
+    return Leaderboard(
+        value=dataframe,
+        datatype=[c.type for c in fields(AutoEvalColumn)],
+        select_columns=SelectColumns(
+            default_selection=default_selection or [c.name for c in fields(AutoEvalColumn) if c.displayed_by_default],
+            cant_deselect=[c.name for c in fields(AutoEvalColumn) if c.never_hidden],
+            label="Select Columns to Display:",
+        ),
+        search_columns=[AutoEvalColumn.model.name, AutoEvalColumn.license.name],
+        hide_columns=hidden_columns or [c.name for c in fields(AutoEvalColumn) if c.hidden],
+        filter_columns=[
+            ColumnFilter(AutoEvalColumn.fewshot_type.name, type="checkboxgroup", label="N-Few-Shot Learning (FS)"),
+            ColumnFilter(AutoEvalColumn.params.name, type="slider", min=0, max=150, label="Select the number of parameters (B)"),
+        ],
+        bool_checkboxgroup_label="Hide models",
+        interactive=False,
+    )
+'''
+
+def download_snapshot(repo, local_dir):
+    """Try to download a snapshot from Hugging Face Hub."""
+    try:
+        print(f"Downloading from {repo} to {local_dir}...")
+        snapshot_download(repo_id=repo, local_dir=local_dir, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN)
+    except Exception as e:
+        print(f"Error downloading {repo}: {e}")
+        restart_space()
+
+
+# Initialize the app by downloading snapshots
+#download_snapshot(QUEUE_REPO, EVAL_REQUESTS_PATH)
+#download_snapshot(RESULTS_REPO, EVAL_RESULTS_PATH)
+
+# Load leaderboard data
+LEADERBOARD_DF = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS)
+finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)
+
+# Prepare the main interface
+demo = gr.Blocks(css=custom_css)
+with demo:
+    gr.HTML(TITLE)
+    gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
+
+    with gr.Tabs(elem_classes="tab-buttons") as tabs:
+
+        # Main leaderboard tab
+        with gr.TabItem("🏅 Benchmark"):
+
+            leaderboard = init_leaderboard(
+                LEADERBOARD_DF,
+                default_selection=['LANG','FS', 'Model', "Avg. Combined Performance ⬆️", "TE", "SA", "HS", "AT", "WIC", "FAQ", "LS", "SU", "NER", "REL"],
+                hidden_columns=[col for col in LEADERBOARD_DF.columns if col not in ['LANG','FS', 'Model', "Avg. Combined Performance ⬆️", "TE", "SA", "HS", "AT", "WIC", "FAQ", "LS", "SU", "NER", "REL"]]
+            )
+
+        # About tab
+        with gr.TabItem("📝 About"):
+            gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
+
+        # About tab
+        with gr.TabItem("║", interactive=False):
+            gr.Markdown("", elem_classes="markdown-text")
+
+        # Task-specific leaderboards
+        for task, metadata in TASK_METADATA_MULTIPLECHOICE.items():
+
+            with gr.TabItem(f"{metadata['icon']}{task}"):
+
+                task_description = TASK_DESCRIPTIONS.get(task, "Description not available.")
+                gr.Markdown(task_description, elem_classes="markdown-text")
+
+                leaderboard = update_task_leaderboard(
+                    LEADERBOARD_DF.rename(columns={f"{task} Prompt Average": "Prompt Average", f"{task} Best Prompt": "Best Prompt", f"{task} Best Prompt Id": "Best Prompt Id", task: "Combined Performance"}),
+                    default_selection=['LANG','FS', 'Model', 'Combined Performance', 'Prompt Average', 'Best Prompt', 'Best Prompt Id'],
+                    hidden_columns=[col for col in LEADERBOARD_DF.columns if col not in ['LANG','FS', 'Model', 'Combined Performance', 'Prompt Average', 'Best Prompt', 'Best Prompt Id']]
+                )
+
+        # About tab
+        with gr.TabItem("│", interactive=False):
+            gr.Markdown("", elem_classes="markdown-text")
+
+        # Task-specific leaderboards
+        for task, metadata in TASK_METADATA_GENERATIVE.items():
+            with gr.TabItem(f"{metadata['icon']}{task}"):
+                task_description = TASK_DESCRIPTIONS.get(task, "Description not available.")
+                gr.Markdown(task_description, elem_classes="markdown-text")
+
+                leaderboard = update_task_leaderboard(
+                    LEADERBOARD_DF.rename(columns={f"{task} Prompt Average": "Prompt Average",
+                                                   f"{task} Best Prompt": "Best Prompt",
+                                                   f"{task} Best Prompt Id": "Best Prompt Id",
+                                                   task: "Combined Performance"}),
+                    default_selection=['LANG','FS', 'Model', 'Combined Performance', 'Prompt Average', 'Best Prompt',
+                                       'Best Prompt Id'],
+                    hidden_columns=[col for col in LEADERBOARD_DF.columns if
+                                    col not in ['LANG','FS', 'Model', 'Combined Performance', 'Prompt Average',
+                                                'Best Prompt', 'Best Prompt Id']]
+                )
+
+    # Citation section
+    with gr.Accordion("📙 Citation", open=False):
+        gr.Textbox(value=CITATION_BUTTON_TEXT, label=CITATION_BUTTON_LABEL, lines=20, elem_id="citation-button", show_copy_button=True)
+
+# Background job to restart space
+scheduler = BackgroundScheduler()
+scheduler.add_job(restart_space, "interval", seconds=1800)
+scheduler.start()
+
+# Launch the app with concurrent queueing
+demo.queue(default_concurrency_limit=40).launch(debug=True,  # Enable Gradio debug mode
+        show_error=True)

e3c_llm_requests/meta-llama/.ipynb_checkpoints/Llama-3.2-1B-Instruct-checkpoint.json

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+{
+    "model": "meta-llama/Llama-3.2-1B-Instruct",
+    "base_model": "LlamaForCausalLM",
+    "revision": "9213176726f574b556790deb65791e0c5aa438b6",
+    "submitted_time": "2024-09-18 15:12:47+00:00",
+    "num_params_billion": 1.2358144,
+    "language": "en_de_fr_it_pt_hi_es_th"
+}

e3c_llm_requests/meta-llama/Llama-3.2-1B-Instruct.json

@@ -0,0 +1,8 @@
+{
+    "model": "meta-llama/Llama-3.2-1B-Instruct",
+    "base_model": "LlamaForCausalLM",
+    "revision": "9213176726f574b556790deb65791e0c5aa438b6",
+    "submitted_time": "2024-09-18 15:12:47+00:00",
+    "num_params_billion": 1.2358144,
+    "language": "en_de_fr_it_pt_hi_es_th"
+}

e3c_llm_results/meta-llama/.ipynb_checkpoints/Llama-3-1.2-1B-Instruct_5_it-checkpoint.json

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+{
+    "average_CPS": 12.479999999999999,
+    "config": {
+        "model_name": "meta-llama/Llama-3.2-1B-Instruct",
+        "num_fewshot": "5",
+        "LANG":"EN",
+        "batch_size": 8
+    },
+    "tasks": {
+        "RE": {
+            "prompts": [
+                {
+                    "prompt": "prom_1",
+                    "metric": "f1",
+                    "value": 12.479999999999999,
+                    "stderr": null
+                }
+            ],
+            "average_accuracy": 12.479999999999999,
+            "best_prompt": 12.479999999999999,
+            "prompt_id": "prom_1",
+            "CPS": 12.479999999999999
+        },
+        "NER": {
+            "prompts": [
+                {
+                    "prompt": "prom_1",
+                    "metric": "f1",
+                    "value": 20,
+                    "stderr": null
+                }
+            ],
+            "average_accuracy": 20,
+            "best_prompt": 20,
+            "prompt_id": "prom_3",
+            "CPS": 20
+        }
+    }
+}

e3c_llm_results/meta-llama/.ipynb_checkpoints/Llama-3-1.2-1B-Instruct_5_sl-checkpoint.json

@@ -0,0 +1,39 @@
+{
+    "average_CPS": 5,
+    "config": {
+        "model_name": "meta-llama/Llama-3.2-1B-Instruct",
+        "num_fewshot": "5",
+        "LANG":"IT",
+        "batch_size": 8
+    },
+    "tasks": {
+        "RE": {
+            "prompts": [
+                {
+                    "prompt": "prom_1",
+                    "metric": "f1",
+                    "value": 5,
+                    "stderr": null
+                }
+            ],
+            "average_accuracy": 5,
+            "best_prompt": 5,
+            "prompt_id": "prom_1",
+            "CPS": 5
+        },
+        "NER": {
+            "prompts": [
+                {
+                    "prompt": "prom_1",
+                    "metric": "f1",
+                    "value": 25,
+                    "stderr": null
+                }
+            ],
+            "average_accuracy": 25,
+            "best_prompt": 25,
+            "prompt_id": "prom_3",
+            "CPS": 25
+        }
+    }
+}

e3c_llm_results/meta-llama/.ipynb_checkpoints/Llama-3.2-1B-Instruct_5-checkpoint.json

@@ -0,0 +1,24 @@
+{
+    "average_CPS": 12.479999999999999,
+    "config": {
+        "model_name": "meta-llama/Llama-3.2-1B-Instruct",
+        "num_fewshot": "5",
+        "batch_size": 8
+    },
+    "tasks": {
+        "RE": {
+            "prompts": [
+                {
+                    "prompt": "prom_1",
+                    "metric": "f1",
+                    "value": 12.479999999999999,
+                    "stderr": null
+                }
+            ],
+            "average_accuracy": 12.479999999999999,
+            "best_prompt": 12.479999999999999,
+            "prompt_id": "prom_1",
+            "CPS": 12.479999999999999
+        }
+    }
+}

e3c_llm_results/meta-llama/Llama-3-1.2-1B-Instruct_5_it.json

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+{
+    "average_CPS": 5,
+    "config": {
+        "model_name": "meta-llama/Llama-3.2-1B-Instruct",
+        "num_fewshot": "5",
+        "LANG":"IT",
+        "batch_size": 8
+    },
+    "tasks": {
+        "RE": {
+            "prompts": [
+                {
+                    "prompt": "prom_1",
+                    "metric": "f1",
+                    "value": 5,
+                    "stderr": null
+                }
+            ],
+            "average_accuracy": 5,
+            "best_prompt": 5,
+            "prompt_id": "prom_1",
+            "CPS": 5
+        },
+        "NER": {
+            "prompts": [
+                {
+                    "prompt": "prom_1",
+                    "metric": "f1",
+                    "value": 25,
+                    "stderr": null
+                }
+            ],
+            "average_accuracy": 25,
+            "best_prompt": 25,
+            "prompt_id": "prom_3",
+            "CPS": 25
+        }
+    }
+}

e3c_llm_results/meta-llama/Llama-3-1.2-1B-Instruct_5_sl.json

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+{
+    "average_CPS": 5,
+    "config": {
+        "model_name": "meta-llama/Llama-3.2-1B-Instruct",
+        "num_fewshot": "5",
+        "LANG":"SL",
+        "batch_size": 8
+    },
+    "tasks": {
+        "RE": {
+            "prompts": [
+                {
+                    "prompt": "prom_1",
+                    "metric": "f1",
+                    "value": 8,
+                    "stderr": null
+                }
+            ],
+            "average_accuracy": 8,
+            "best_prompt": 8,
+            "prompt_id": "prom_1",
+            "CPS": 8
+        },
+        "NER": {
+            "prompts": [
+                {
+                    "prompt": "prom_1",
+                    "metric": "f1",
+                    "value": 28,
+                    "stderr": null
+                }
+            ],
+            "average_accuracy": 28,
+            "best_prompt": 28,
+            "prompt_id": "prom_3",
+            "CPS": 28
+        }
+    }
+}

e3c_llm_results/meta-llama/Llama-3.2-1B-Instruct_5.json

@@ -0,0 +1,39 @@
+{
+    "average_CPS": 12.479999999999999,
+    "config": {
+        "model_name": "meta-llama/Llama-3.2-1B-Instruct",
+        "num_fewshot": "5",
+        "LANG":"EN",
+        "batch_size": 8
+    },
+    "tasks": {
+        "RE": {
+            "prompts": [
+                {
+                    "prompt": "prom_1",
+                    "metric": "f1",
+                    "value": 12.479999999999999,
+                    "stderr": null
+                }
+            ],
+            "average_accuracy": 12.479999999999999,
+            "best_prompt": 12.479999999999999,
+            "prompt_id": "prom_1",
+            "CPS": 12.479999999999999
+        },
+        "NER": {
+            "prompts": [
+                {
+                    "prompt": "prom_1",
+                    "metric": "f1",
+                    "value": 20,
+                    "stderr": null
+                }
+            ],
+            "average_accuracy": 20,
+            "best_prompt": 20,
+            "prompt_id": "prom_3",
+            "CPS": 20
+        }
+    }
+}

example_app.py

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+import gradio as gr
+from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns
+import pandas as pd
+from apscheduler.schedulers.background import BackgroundScheduler
+from huggingface_hub import snapshot_download
+
+from src.about import (
+    CITATION_BUTTON_LABEL,
+    CITATION_BUTTON_TEXT,
+    EVALUATION_QUEUE_TEXT,
+    INTRODUCTION_TEXT,
+    LLM_BENCHMARKS_TEXT,
+    TITLE,
+)
+
+from src.tasks import (
+    TE_DESCRIPTION,
+)
+
+from src.display.css_html_js import custom_css
+from src.display.utils import (
+    BENCHMARK_COLS,
+    COLS,
+    EVAL_COLS,
+    EVAL_TYPES,
+    AutoEvalColumn,
+    ModelType,
+    fields,
+    WeightType,
+    Precision
+)
+from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, QUEUE_REPO, REPO_ID, RESULTS_REPO, TOKEN
+from src.populate import get_evaluation_queue_df, get_leaderboard_df
+from src.submission.submit import add_new_eval
+
+
+def restart_space():
+    API.restart_space(repo_id=REPO_ID)
+
+### Space initialisation
+try:
+    print(EVAL_REQUESTS_PATH)
+    snapshot_download(
+        repo_id=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN
+    )
+except Exception:
+    restart_space()
+try:
+    print(EVAL_RESULTS_PATH)
+    snapshot_download(
+        repo_id=RESULTS_REPO, local_dir=EVAL_RESULTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN
+    )
+except Exception:
+    restart_space()
+
+
+LEADERBOARD_DF = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS)
+
+(
+    finished_eval_queue_df,
+    running_eval_queue_df,
+    pending_eval_queue_df,
+) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)
+
+def init_leaderboard(dataframe):
+    print(dataframe)
+    if dataframe is None or dataframe.empty:
+        raise ValueError("Leaderboard DataFrame is empty or None.")
+    return Leaderboard(
+        value=dataframe,
+        datatype=[c.type for c in fields(AutoEvalColumn)],
+        select_columns=SelectColumns(
+            default_selection=[c.name for c in fields(AutoEvalColumn) if c.displayed_by_default],
+            cant_deselect=[c.name for c in fields(AutoEvalColumn) if c.never_hidden],
+            label="Select Columns to Display:",
+        ),
+        search_columns=[AutoEvalColumn.model.name, AutoEvalColumn.license.name],
+        hide_columns=[c.name for c in fields(AutoEvalColumn) if c.hidden],
+        filter_columns=[
+            ColumnFilter(AutoEvalColumn.model_type.name, type="checkboxgroup", label="Model types"),
+            ColumnFilter(AutoEvalColumn.precision.name, type="checkboxgroup", label="Precision"),
+            ColumnFilter(
+                AutoEvalColumn.params.name,
+                type="slider",
+                min=0.01,
+                max=150,
+                label="Select the number of parameters (B)",
+            ),
+            ColumnFilter(
+                AutoEvalColumn.still_on_hub.name, type="boolean", label="Deleted/incomplete", default=True
+            ),
+        ],
+        bool_checkboxgroup_label="Hide models",
+        interactive=False,
+    )
+
+
+def init_leaderboard2(dataframe, default_selection=None, hidden_columns=None):
+
+    print("entrato===============================================")
+
+    if dataframe is None or dataframe.empty:
+        raise ValueError("Leaderboard DataFrame is empty or None.")
+    return Leaderboard(
+        value=dataframe,
+        datatype=[c.type for c in fields(AutoEvalColumn)],
+        select_columns=SelectColumns(
+            default_selection=default_selection or [c.name for c in fields(AutoEvalColumn) if c.displayed_by_default],
+            cant_deselect=[c.name for c in fields(AutoEvalColumn) if c.never_hidden],
+            label="Select Columns to Display:",
+        ),
+        search_columns=[AutoEvalColumn.model.name, AutoEvalColumn.license.name],
+        hide_columns=hidden_columns or [c.name for c in fields(AutoEvalColumn) if c.hidden],
+        filter_columns=[
+            ColumnFilter(AutoEvalColumn.model_type.name, type="checkboxgroup", label="Model types"),
+            ColumnFilter(AutoEvalColumn.precision.name, type="checkboxgroup", label="Precision"),
+            ColumnFilter(AutoEvalColumn.params.name, type="slider", min=0.01, max=150, label="Select the number of parameters (B)"),
+            ColumnFilter(AutoEvalColumn.still_on_hub.name, type="boolean", label="Deleted/incomplete", default=True),
+        ],
+        bool_checkboxgroup_label="Hide models",
+        interactive=False,
+    )
+
+
+demo = gr.Blocks(css=custom_css)
+with demo:
+    gr.HTML(TITLE)
+    gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
+
+    with gr.Tabs(elem_classes="tab-buttons") as tabs:
+        with gr.TabItem("🏅 EVALITA-LLM Benchmark", elem_id="llm-benchmark-tab-table", id=0):
+            #leaderboard = init_leaderboard(LEADERBOARD_DF)
+
+            leaderboard = init_leaderboard2(
+                LEADERBOARD_DF,
+                default_selection=['T', 'Model', "Average ⬆��", "TE", "SA", "HS", "AT", "WIC", "FAQ", "LS", "SU", "NER", "REL"],
+                hidden_columns=[col for col in LEADERBOARD_DF.columns if
+                                col not in ['T', 'Model', "Average ⬆️", "TE", "SA", "HS", "AT", "WIC", "FAQ", "LS", "SU", "NER", "REL" ]]
+            )
+
+
+        with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=2):
+            gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
+
+        with gr.TabItem("🚀 Submit here! ", elem_id="llm-benchmark-tab-table", id=3):
+            with gr.Column():
+                with gr.Row():
+                    gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text")
+
+                with gr.Column():
+                    with gr.Accordion(
+                        f"✅ Finished Evaluations ({len(finished_eval_queue_df)})",
+                        open=False,
+                    ):
+                        with gr.Row():
+                            finished_eval_table = gr.components.Dataframe(
+                                value=finished_eval_queue_df,
+                                headers=EVAL_COLS,
+                                datatype=EVAL_TYPES,
+                                row_count=5,
+                            )
+                    with gr.Accordion(
+                        f"🔄 Running Evaluation Queue ({len(running_eval_queue_df)})",
+                        open=False,
+                    ):
+                        with gr.Row():
+                            running_eval_table = gr.components.Dataframe(
+                                value=running_eval_queue_df,
+                                headers=EVAL_COLS,
+                                datatype=EVAL_TYPES,
+                                row_count=5,
+                            )
+
+                    with gr.Accordion(
+                        f"⏳ Pending Evaluation Queue ({len(pending_eval_queue_df)})",
+                        open=False,
+                    ):
+                        with gr.Row():
+                            pending_eval_table = gr.components.Dataframe(
+                                value=pending_eval_queue_df,
+                                headers=EVAL_COLS,
+                                datatype=EVAL_TYPES,
+                                row_count=5,
+                            )
+            with gr.Row():
+                gr.Markdown("# ✉️✨ Submit your model here!", elem_classes="markdown-text")
+
+            with gr.Row():
+                with gr.Column():
+                    model_name_textbox = gr.Textbox(label="Model name")
+                    revision_name_textbox = gr.Textbox(label="Revision commit", placeholder="main")
+                    model_type = gr.Dropdown(
+                        choices=[t.to_str(" : ") for t in ModelType if t != ModelType.Unknown],
+                        label="Model type",
+                        multiselect=False,
+                        value=None,
+                        interactive=True,
+                    )
+
+                with gr.Column():
+                    precision = gr.Dropdown(
+                        choices=[i.value.name for i in Precision if i != Precision.Unknown],
+                        label="Precision",
+                        multiselect=False,
+                        value="float16",
+                        interactive=True,
+                    )
+                    weight_type = gr.Dropdown(
+                        choices=[i.value.name for i in WeightType],
+                        label="Weights type",
+                        multiselect=False,
+                        value="Original",
+                        interactive=True,
+                    )
+                    base_model_name_textbox = gr.Textbox(label="Base model (for delta or adapter weights)")
+
+            submit_button = gr.Button("Submit Eval")
+            submission_result = gr.Markdown()
+            submit_button.click(
+                add_new_eval,
+                [
+                    model_name_textbox,
+                    base_model_name_textbox,
+                    revision_name_textbox,
+                    precision,
+                    weight_type,
+                    model_type,
+                ],
+                submission_result,
+            )
+
+
+        with gr.TabItem("TE", elem_id="llm-benchmark-tab-table", id=4):
+            gr.Markdown(TE_DESCRIPTION, elem_classes="markdown-text")
+            #leaderboard = init_leaderboard(LEADERBOARD_DF)
+
+            LEADERBOARD_DF_TE = LEADERBOARD_DF.rename(columns={"TE Prompt Average": "Prompt Average",
+                                                            "TE Best Prompt": "Best Prompt",
+                                                            "TE Best Prompt Id": "Best Prompt Id",
+                                                            "TE": "Combined Performance"})
+
+            leaderboard = init_leaderboard2(
+                LEADERBOARD_DF_TE,
+                default_selection=['T', 'Model', 'Combined Performance', 'Prompt Average', 'Best Prompt', 'Best Prompt Id'],
+                hidden_columns=[col for col in LEADERBOARD_DF.columns if
+                                col not in ['T', 'Model', 'Combined Performance', 'Prompt Average', 'Best Prompt', 'Best Prompt Id']]
+            )
+
+
+        with gr.TabItem("SA", elem_id="llm-benchmark-tab-table", id=5):
+            gr.Markdown(TE_DESCRIPTION, elem_classes="markdown-text")
+
+            LEADERBOARD_DF_SA = LEADERBOARD_DF.rename(columns={"SA Prompt Average": "Prompt Average",
+                                                            "SA Best Prompt": "Best Prompt",
+                                                            "SA Best Prompt Id": "Best Prompt Id",
+                                                            "SA": "Combined Performance"})
+
+            leaderboard = init_leaderboard2(
+                LEADERBOARD_DF_SA,
+                default_selection=['T', 'Model', 'Combined Performance', 'Prompt Average', 'Best Prompt',
+                                   'Best Prompt Id'],
+                hidden_columns=[col for col in LEADERBOARD_DF.columns if
+                                col not in ['T', 'Model', 'Combined Performance', 'Prompt Average', 'Best Prompt',
+                                            'Best Prompt Id']]
+            )
+
+
+
+
+        with gr.TabItem("HS", elem_id="llm-benchmark-tab-table", id=6):
+            gr.Markdown(TE_DESCRIPTION, elem_classes="markdown-text")
+
+            LEADERBOARD_DF_HS = LEADERBOARD_DF.rename(columns={"HS Prompt Average": "Prompt Average",
+                                                               "HS Best Prompt": "Best Prompt",
+                                                               "HS Best Prompt Id": "Best Prompt Id",
+                                                               "HS": "Combined Performance"})
+
+            leaderboard = init_leaderboard2(
+                LEADERBOARD_DF_HS,
+                default_selection=['T', 'Model', 'Combined Performance', 'Prompt Average', 'Best Prompt',
+                                   'Best Prompt Id'],
+                hidden_columns=[col for col in LEADERBOARD_DF.columns if
+                                col not in ['T', 'Model', 'Combined Performance', 'Prompt Average', 'Best Prompt',
+                                            'Best Prompt Id']]
+            )
+
+
+
+        with gr.TabItem("AT", elem_id="llm-benchmark-tab-table", id=7):
+            gr.Markdown(TE_DESCRIPTION, elem_classes="markdown-text")
+
+        with gr.TabItem("WIC", elem_id="llm-benchmark-tab-table", id=8):
+            gr.Markdown(TE_DESCRIPTION, elem_classes="markdown-text")
+
+        with gr.TabItem("FAQ", elem_id="llm-benchmark-tab-table", id=9):
+            gr.Markdown(TE_DESCRIPTION, elem_classes="markdown-text")
+
+        with gr.TabItem("LS", elem_id="llm-benchmark-tab-table", id=10):
+            gr.Markdown(TE_DESCRIPTION, elem_classes="markdown-text")
+
+        with gr.TabItem("SU", elem_id="llm-benchmark-tab-table", id=11):
+            gr.Markdown(TE_DESCRIPTION, elem_classes="markdown-text")
+
+        with gr.TabItem("NER", elem_id="llm-benchmark-tab-table", id=12):
+            gr.Markdown(TE_DESCRIPTION, elem_classes="markdown-text")
+
+        with gr.TabItem("REL", elem_id="llm-benchmark-tab-table", id=13):
+            gr.Markdown(TE_DESCRIPTION, elem_classes="markdown-text")
+
+
+    with gr.Row():
+        with gr.Accordion("📙 Citation", open=False):
+            citation_button = gr.Textbox(
+                value=CITATION_BUTTON_TEXT,
+                label=CITATION_BUTTON_LABEL,
+                lines=20,
+                elem_id="citation-button",
+                show_copy_button=True,
+            )
+
+scheduler = BackgroundScheduler()
+scheduler.add_job(restart_space, "interval", seconds=1800)
+scheduler.start()
+demo.queue(default_concurrency_limit=40).launch()

example_app2.py

@@ -0,0 +1,216 @@
+import gradio as gr
+from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns
+import pandas as pd
+from apscheduler.schedulers.background import BackgroundScheduler
+from huggingface_hub import snapshot_download
+
+from src.about import (
+    CITATION_BUTTON_LABEL, CITATION_BUTTON_TEXT, EVALUATION_QUEUE_TEXT,
+    INTRODUCTION_TEXT, LLM_BENCHMARKS_TEXT, TITLE
+)
+from src.tasks import TASK_DESCRIPTIONS, MEASURE_DESCRIPTION
+from src.display.css_html_js import custom_css
+from src.display.utils import (
+    BENCHMARK_COLS, COLS, EVAL_COLS, EVAL_TYPES, AutoEvalColumn,
+    ModelType, fields, WeightType, Precision
+)
+from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, QUEUE_REPO, REPO_ID, RESULTS_REPO, TOKEN
+from src.populate import get_evaluation_queue_df, get_leaderboard_df
+from src.submission.submit import add_new_eval
+
+
+
+
+# Define the task icons and names
+TASK_ICONS = {
+    "TE": "📊",  # Textual Entailment
+    "SA": "😃",  # Sentiment Analysis
+    "HS": "⚠️",  # Hate Speech
+    "AT": "🏥",  # Admission Test
+    "WIC": "🔤",  # Word in Context
+    "FAQ": "❓",  # Frequently Asked Questions
+    "LS": "🔄",  # Lexical Substitution
+    "SU": "📝",  # Summarization
+    "NER": "🏷️",  # Named Entity Recognition
+    "REL": "🔗",  # Relation Extraction
+}
+
+TASK_NAMES = {
+    "TE": "Textual Entailment",
+    "SA": "Sentiment Analysis",
+    "HS": "Hate Speech",
+    "AT": "Admission Test",
+    "WIC": "Word in Context",
+    "FAQ": "Frequently Asked Questions",
+    "LS": "Lexical Substitution",
+    "SU": "Summarization",
+    "NER": "Named Entity Recognition",
+    "REL": "Relation Extraction",
+}
+
+
+# Tooltip descriptions for each task
+TASK_TOOLTIPS = {
+    "TE": "Identify logical relationships between two text segments.",
+    "SA": "Classify the sentiment (positive, negative, neutral) of a text.",
+    "HS": "Detect hate speech in a text.",
+    "AT": "Classify whether a clinical statement pertains to an admission test.",
+    "WIC": "Identify words in context and their meaning.",
+    "FAQ": "Answer frequently asked questions based on given text.",
+    "LS": "Identify alternative words in a given context.",
+    "SU": "Summarize long text into a shorter version.",
+    "NER": "Identify named entities (e.g., persons, locations, organizations) in text.",
+    "REL": "Extract and link laboratory test results to the respective tests in clinical narratives.",
+}
+
+
+
+
+def restart_space():
+    """Restart the Hugging Face space."""
+    API.restart_space(repo_id=REPO_ID)
+
+
+def download_snapshot(repo, local_dir):
+    """Try to download a snapshot from the Hugging Face Hub, restarting space on failure."""
+    try:
+        print(f"Downloading from {repo} to {local_dir}...")
+        snapshot_download(repo_id=repo, local_dir=local_dir, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN)
+    except Exception as e:
+        print(f"Error downloading {repo}: {e}")
+        restart_space()
+
+
+# Space initialization
+download_snapshot(QUEUE_REPO, EVAL_REQUESTS_PATH)
+download_snapshot(RESULTS_REPO, EVAL_RESULTS_PATH)
+
+# Load leaderboard and evaluation queue data
+LEADERBOARD_DF = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS)
+finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)
+
+
+def init_leaderboard(dataframe, default_selection=None, hidden_columns=None):
+    """Initialize a leaderboard with specific columns."""
+    if dataframe is None or dataframe.empty:
+        raise ValueError("Leaderboard DataFrame is empty or None.")
+
+    return Leaderboard(
+        value=dataframe,
+        datatype=[c.type for c in fields(AutoEvalColumn)],
+        select_columns=SelectColumns(
+            default_selection=default_selection or [c.name for c in fields(AutoEvalColumn) if c.displayed_by_default],
+            cant_deselect=[c.name for c in fields(AutoEvalColumn) if c.never_hidden],
+            label="Select Columns to Display:",
+        ),
+        search_columns=[AutoEvalColumn.model.name, AutoEvalColumn.license.name],
+        hide_columns=hidden_columns or [c.name for c in fields(AutoEvalColumn) if c.hidden],
+        filter_columns=[
+            #ColumnFilter(AutoEvalColumn.model_type.name, type="checkboxgroup", label="Model types"),
+            ColumnFilter(AutoEvalColumn.fewshot_type.name, type="checkboxgroup", label="Few-Shot Learning (FS)"),
+            #ColumnFilter(AutoEvalColumn.precision.name, type="checkboxgroup", label="Precision"),
+            ColumnFilter(AutoEvalColumn.params.name, type="slider", min=0.01, max=150, label="Select the number of parameters (B)"),
+            #ColumnFilter(AutoEvalColumn.still_on_hub.name, type="boolean", label="Deleted/incomplete", default=True),
+        ],
+        bool_checkboxgroup_label="Hide models",
+        interactive=False,
+    )
+
+
+def prepare_leaderboard_df(df, task_prefix):
+    """Rename columns for a specific task to a standard format."""
+    return df.rename(columns={
+        f"{task_prefix} Prompt Average": "Prompt Average",
+        f"{task_prefix} Best Prompt": "Best Prompt",
+        f"{task_prefix} Best Prompt Id": "Best Prompt Id",
+        task_prefix: "Combined Performance"
+    })
+
+
+demo = gr.Blocks(css=custom_css)
+with demo:
+    gr.HTML(TITLE)
+    gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
+
+    with gr.Tabs(elem_classes="tab-buttons") as tabs:
+        # Main leaderboard tab
+        with gr.TabItem("🏅 EVALITA-LLM Benchmark", elem_id="llm-benchmark-tab-table"):
+            leaderboard = init_leaderboard(
+                LEADERBOARD_DF,
+                default_selection=['FS', 'Model', "Avg. Combined Performance ⬆️", "TE", "SA", "HS", "AT", "WIC", "FAQ", "LS", "SU", "NER", "REL"],
+                hidden_columns=[col for col in LEADERBOARD_DF.columns if col not in
+                                ['FS', 'Model', "Avg. Combined Performance ⬆️", "TE", "SA", "HS", "AT", "WIC", "FAQ", "LS", "SU", "NER", "REL"]]
+            )
+
+        # About tab
+        with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table"):
+            gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
+
+        '''
+        # Submission tab
+        with gr.TabItem("🚀 Submit here! ", elem_id="llm-benchmark-tab-table"):
+            gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text")
+
+            for queue_name, queue_df in [
+                ("✅ Finished Evaluations", finished_eval_queue_df),
+                ("🔄 Running Evaluation Queue", running_eval_queue_df),
+                ("⏳ Pending Evaluation Queue", pending_eval_queue_df)
+            ]:
+                with gr.Accordion(f"{queue_name} ({len(queue_df)})", open=False):
+                    gr.components.Dataframe(value=queue_df, headers=EVAL_COLS, datatype=EVAL_TYPES, row_count=5)
+
+            gr.Markdown("# ✉️✨ Submit your model here!", elem_classes="markdown-text")
+            with gr.Row():
+                model_name_textbox = gr.Textbox(label="Model name")
+                revision_name_textbox = gr.Textbox(label="Revision commit", placeholder="main")
+                model_type = gr.Dropdown(choices=[t.to_str(" : ") for t in ModelType if t != ModelType.Unknown],
+                                         label="Model type", multiselect=False, interactive=True)
+                precision = gr.Dropdown(choices=[i.value.name for i in Precision if i != Precision.Unknown],
+                                        label="Precision", multiselect=False, value="float16", interactive=True)
+                weight_type = gr.Dropdown(choices=[i.value.name for i in WeightType],
+                                          label="Weights type", multiselect=False, value="Original", interactive=True)
+                base_model_name_textbox = gr.Textbox(label="Base model (for delta or adapter weights)")
+
+            submit_button = gr.Button("Submit Eval")
+            submission_result = gr.Markdown()
+            submit_button.click(
+                add_new_eval,
+                [model_name_textbox, base_model_name_textbox, revision_name_textbox, precision, weight_type, model_type],
+                submission_result,
+            )
+        '''
+
+        # Task-specific leaderboards
+        for task in ["TE", "SA", "HS", "AT", "WIC", "FAQ", "LS", "SU", "NER", "REL"]:
+
+            with gr.TabItem(f"{TASK_ICONS[task]}{task}", elem_id="llm-benchmark-tab-table"):
+
+                task_description = TASK_DESCRIPTIONS.get(task, "Description not available.")
+
+
+
+
+                gr.Markdown(task_description, elem_classes="markdown-text")
+
+
+                gr.Markdown(MEASURE_DESCRIPTION, elem_classes="markdown-text")
+
+
+
+                leaderboard = init_leaderboard(
+                    prepare_leaderboard_df(LEADERBOARD_DF, task),
+                    default_selection=['FS', 'Model', 'Combined Performance', 'Prompt Average', 'Best Prompt', 'Best Prompt Id'],
+                    hidden_columns=[col for col in LEADERBOARD_DF.columns if col not in
+                                    ['FS', 'Model', 'Combined Performance', 'Prompt Average', 'Best Prompt', 'Best Prompt Id']]
+                )
+
+    # Citation section
+    with gr.Accordion("📙 Citation", open=False):
+        gr.Textbox(value=CITATION_BUTTON_TEXT, label=CITATION_BUTTON_LABEL, lines=20, elem_id="citation-button", show_copy_button=True)
+
+# Background job to restart space
+scheduler = BackgroundScheduler()
+scheduler.add_job(restart_space, "interval", seconds=1800)
+scheduler.start()
+
+demo.queue(default_concurrency_limit=40).launch()

get_model_info.py

@@ -0,0 +1,129 @@
+"""
+MODEL METADATA EXTRACTOR
+
+This script processes model evaluation output files (input_folder) from the lm-eval-harness library,
+extracts model identifiers, retrieves detailed metadata from HuggingFace
+and saves the information as structured JSON files (output_folder).
+
+Input: Directory containing .out files from lm-eval-harness
+Output: Directory with JSON files containing model metadata
+"""
+
+# Example input file format (lm-eval-harness output):
+'''
+hf (pretrained=swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA,trust_remote_code=True), gen_kwargs: (None), limit: None, num_fewshot: 5, batch_size: 1
+|         Tasks          |Version|Filter|n-shot| Metric |   |Value |   |Stderr|
+|------------------------|------:|------|-----:|--------|---|-----:|---|------|
+|evalita-mp              |      1|none  |      |acc     |↑  |0.5605|±  |0.0052|
+...
+Job completed
+'''
+
+# Example output JSON format:
+'''
+{
+    "model": "swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA",
+    "base_model": "LlamaForCausalLM",
+    "revision": "2b6e46e4c9d341dc8bf8350a167492c880116b66",
+    "submitted_time": "2024-04-29 09:34:12+00:00",
+    "num_params_billion": 8.030261248,
+    "language": "en_it"
+}
+'''
+
+import os
+import re
+import json
+from huggingface_hub import HfApi
+
+# Configures the Hugging Face token (if needed)
+# TOKEN = "YOUR_HUGGINGFACE_API_TOKEN"
+api = HfApi()
+
+# Directory paths
+# input_folder: Directory containing the output files of the lm-eval-harness library, including model accuracy metrics.
+#input_folder = "../evalita_llm_models_output/"
+input_folder = "/home/sfarzi/leaderboard/evalita_llm_leaderboard/task_result/"
+# output_folder: Directory where JSON files with model characteristics will be saved.
+output_folder = "/home/sfarzi/leaderboard/evalita_llm_leaderboard/e3c_llm_requests/"
+
+# Creates the output folder if it doesn't exist
+os.makedirs(output_folder, exist_ok=True)
+
+# Regular expression to find the model name
+model_pattern = re.compile(r"pretrained=([\w\-./]+)")
+
+# Scans files in the input folder
+for filename in os.listdir(input_folder):
+    if filename.endswith('.out'):
+        file_path = os.path.join(input_folder, filename)
+
+        # Reads the file content
+        with open(file_path, "r", encoding="utf-8") as f:
+            content = f.read()
+
+        # Extracts the model name
+        match = model_pattern.search(content)
+        if match:
+            model_name = match.group(1)
+            print(f"Processing model: {model_name}")
+
+            try:
+                # Retrieves model information from HuggingFace
+                model_info = api.model_info(model_name)
+
+                # Calculates the number of parameters in billions, if available
+                num_params = None
+                if model_info.safetensors and "BF16" in model_info.safetensors.parameters:
+                    num_params = model_info.safetensors.parameters["BF16"] / 1e9  # Convert to billions
+
+                # Extracts and concatenates languages
+                language = "_".join(model_info.card_data.get("language", [])) if model_info.card_data else ""
+
+                #print(model_info)
+
+                # Builds the dictionary with required metadata
+                model_data = {
+                    "model": model_name,
+                    "base_model": model_info.config.get("architectures", [""])[0] if model_info.config else "",
+                    "revision": model_info.sha,
+                    # "precision": "bfloat16",  # If available, replace with real value
+                    # "weight_type": "Original",
+                    # "status": "FINISHED",
+                    "submitted_time": str(model_info.created_at),
+                    # "model_type": "pretrained",
+                    # "likes": model_info.likes,
+                    # "params": model_info.safetensors_size_in_bytes / 1e9 if model_info.safetensors_size_in_bytes else None,
+                    # "license": model_info.license,
+                    # "private": model_info.private,
+                    "num_params_billion": num_params,  # Number of parameters in billions
+                    "language": language,  # Extracted language
+                }
+
+                # Separates the model_name into two parts: directory name and file name
+                if "/" in model_name:
+                    dir_name, file_name = model_name.split("/", 1)
+                else:
+                    dir_name, file_name = model_name, model_name  # If no "/", use the same name
+
+                # Creates the folder for saving the produced json files
+                model_output_folder = os.path.join(output_folder, dir_name)
+                os.makedirs(model_output_folder, exist_ok=True)
+
+                # Saves the JSON file in the appropriate folder
+                output_file = os.path.join(model_output_folder, f"{file_name}.json")
+
+                # Check if the file already exists
+                if os.path.exists(output_file):
+                    print(f"File {output_file} already exists. Skipping...")
+                    continue
+
+                with open(output_file, "w", encoding="utf-8") as f:
+                    json.dump(model_data, f, indent=4)
+
+                print(f"Saved metadata for {model_name} in {output_file}")
+
+            except Exception as e:
+                print(f"Error retrieving info for {model_name}: {e}")
+
+            print("Process finished!")

preprocess_models_output.py

@@ -0,0 +1,250 @@
+"""
+EVALITA LLM EVALUATION PROCESSOR
+
+Transforms raw model evaluation outputs into structured performance reports for leaderboard integration.
+
+DATA PIPELINE OVERVIEW:
+
+1. Inputs:
+   - Evaluation Results: Raw .out files from lm-eval-harness
+   - Model Metadata: Pre-collected .json files from HuggingFace
+
+2. Output:
+   - Comprehensive evaluation reports in JSON format
+   - Ready for ingestion into the evaluation leaderboard
+
+--------------------------------------------------------------------
+INPUT SPECIFICATION
+
+Evaluation Results (.out format):
+   hf (pretrained=model-org/model-name), num_fewshot: 5, batch_size: 1
+   | Task          | Metric | Value  | Stderr |
+   |---------------|--------|--------|--------|
+   | main-task     | acc    | 0.5605 | 0.0052 |
+   | - sub-task    | acc    | 0.4640 | 0.0088 |
+   |   - prompt-1  | acc    | 0.3720 | 0.0216 |
+
+Model Metadata (.json format):
+   {
+     "model": "model-org/model-name",
+     "base_model": "ModelArchitecture",
+     "revision": "git_commit_hash",
+     "parameters": 8.03,
+     "language": "en_it"
+   }
+
+--------------------------------------------------------------------
+OUTPUT SPECIFICATION
+
+Evaluation Report (.json format):
+   {
+     "summary_metrics": {
+       "average_CPS": 41.74,
+       "num_tasks": 12
+     },
+     "model_config": {
+       "identifier": "model-org/model-name",
+       "architecture": "ModelArchitecture",
+       "parameters": 8.03,
+       "evaluation_settings": {
+         "fewshot": 5,
+         "batch_size": 1
+       }
+     },
+     "task_results": {
+       "task-name": {
+         "average_score": 52.60,
+         "best_prompt": {
+           "id": "prompt-6",
+           "score": 66.57
+         },
+         "prompt_analysis": [
+           {
+             "prompt_id": "prompt-1",
+             "score": 37.20,
+             "stderr": 0.0216
+           }
+         ]
+       }
+     }
+   }
+"""
+
+import json
+import os
+import re
+
+def safe_float(value):
+    """Safely converts a value to float, returning None if the conversion fails."""
+    try:
+        return float(value)
+    except ValueError:
+        return None
+
+
+def calculate_task_metrics(task_info):
+    """Calculates average accuracy, best prompt accuracy, and CPS for a given task."""
+    accuracies = [prompt['value'] for prompt in task_info['prompts'] if prompt['value'] is not None]
+
+    if not accuracies:
+        return None
+
+    task_info['average_accuracy'] = sum(accuracies) / len(accuracies)
+    best_prompt_data = max(task_info['prompts'], key=lambda x: x['value'])
+    task_info['best_prompt'] = best_prompt_data['value']
+    task_info['prompt_id'] = best_prompt_data['prompt']
+
+    # Calculate CPS
+    avg_acc = task_info['average_accuracy']
+    best_acc = task_info['best_prompt']
+    task_info['CPS'] = (1 - (best_acc - avg_acc) / 100) * best_acc
+
+
+def extract_data_from_file(file_path):
+    """Extracts task and prompt data from a specified file."""
+    with open(file_path, 'r') as file:
+        lines = file.readlines()
+
+    tasks_data = {}
+    current_task = None
+
+    for line in lines:
+        line = line.strip()
+
+        # Skips empty lines
+        if not line:
+            continue
+
+        # Skips header lines
+        if line.startswith("|         Tasks"):
+            continue
+
+        # Extracts model configuration details
+        if line.startswith("hf (pretrained="):
+            start = line.find("pretrained=") + len("pretrained=")
+            end = line.find(",", start)
+            pretrained_model = line[start:end]
+
+            num_fewshot_match = re.search(r"num_fewshot:\s*([\w\d]+)", line)
+            num_fewshot = num_fewshot_match.group(1) if num_fewshot_match else None
+
+            batch_size_match = re.search(r"batch_size:\s*(\d+)", line)
+            batch_size = int(batch_size_match.group(1)) if batch_size_match else None
+
+            continue
+
+        columns = line.split('|')
+        if len(columns) != 11:
+            continue
+
+        task_name = columns[1]
+        metric = columns[5].strip()
+        value = safe_float(columns[7])
+        stderr = safe_float(columns[9])
+        print (value)
+        # Skips normalized accuracy metrics
+        if metric == "acc_norm":
+            continue
+
+        # Identifies task and prompt sections in the file
+        if task_name.startswith(" - "):
+            task_name = task_name[3:].strip()
+            current_task = task_name
+            tasks_data.setdefault(current_task,
+                                  {'prompts': [], 'average_accuracy': 0, 'best_prompt': None, 'prompt_id': None,
+                                   'CPS': None})
+
+        elif task_name.startswith("  - ") and current_task:
+            prompt_name = task_name[4:].strip()
+            prompt_data = {'prompt': prompt_name, 'metric': metric, 'value': value * 100,
+                           'stderr': stderr}
+            tasks_data[current_task]['prompts'].append(prompt_data)
+
+    # Special handling for evalita NER task to calculate weighted prompt averages
+    if "evalita NER" in tasks_data:
+        task_info = tasks_data["evalita NER"]
+        weight_map = {"ADG prompt-1": 521, "ADG prompt-2": 521, "FIC prompt-1": 1517, "FIC prompt-2": 1517,
+                      "WN prompt-1": 2088, "WN prompt-2": 2088}
+
+        weighted_values = {"prompt-1": 0, "prompt-2": 0}
+        total_weights = sum(weight_map.values())
+
+        for prompt in task_info['prompts']:
+            if prompt['prompt'] in weight_map:
+                if "prompt-1" in prompt['prompt']:
+                    weighted_values["prompt-1"] += weight_map[prompt['prompt']] * prompt['value']
+                elif "prompt-2" in prompt['prompt']:
+                    weighted_values["prompt-2"] += weight_map[prompt['prompt']] * prompt['value']
+
+        task_info['prompts'] = [
+            {"prompt": "prompt-1", "metric": "acc", "value": weighted_values["prompt-1"] / total_weights,
+             'stderr': None},
+            {"prompt": "prompt-2", "metric": "acc", "value": weighted_values["prompt-2"] / total_weights,
+             'stderr': None}]
+
+    # Calculates task metrics for each task
+    for task_info in tasks_data.values():
+        calculate_task_metrics(task_info)
+
+    # Calculates the average CPS across all tasks
+    tasks_with_cps = [task['CPS'] for task in tasks_data.values() if task['CPS'] is not None]
+    average_CPS = sum(tasks_with_cps) / len(tasks_with_cps) if tasks_with_cps else 0
+
+    config = {
+        "model_name": pretrained_model,
+        "num_fewshot": num_fewshot,
+        "batch_size": batch_size
+    }
+
+    return {'average_CPS': average_CPS, 'config': config, 'tasks': tasks_data}
+
+
+"""
+MAIN PROCESSING PIPELINE
+
+This script executes the complete evaluation data processing workflow:
+
+1. Input Sources:
+   - Raw evaluation results (.out files) from: ../evalita_llm_models_output/
+   - Model metadata JSON files from: ../evalita_llm_requests/
+
+2. Processing Steps:
+   - Parses evaluation metrics from .out files
+   - Combines with model metadata
+   - Calculates aggregated performance statistics
+
+3. Output:
+   - Structured JSON results saved to: ../evalita_llm_results/
+   - Organized by model organization/name
+   - Contains complete evaluation results with metadata
+"""
+directory_in_path = '/home/sfarzi/leaderboard/evalita_llm_leaderboard/task_result/'
+directory_in_requests_path = '/home/sfarzi/leaderboard/evalita_llm_leaderboard/evalita_llm_requests/'
+directory_out_results_path = '/home/sfarzi/leaderboard/evalita_llm_leaderboard/evalita_llm_results/'
+
+for filename in os.listdir(directory_in_path):
+    if filename.endswith('.out'):
+        file_path = os.path.join(directory_in_path, filename)
+        json_output = extract_data_from_file(file_path)
+
+        model_org_name, model_name = json_output['config']['model_name'].split('/')
+
+
+        config_file_path = os.path.join(directory_in_requests_path, model_org_name, f"{model_name}.json")
+
+        if os.path.exists(config_file_path):
+            with open(config_file_path, 'r', encoding='utf-8') as config_file:
+                additional_config = json.load(config_file)
+            json_output['config'].update(additional_config)
+
+
+        org_folder_path = os.path.join(directory_out_results_path, model_org_name)
+        os.makedirs(org_folder_path, exist_ok=True)
+
+        file_suffix = f"{json_output['config']['num_fewshot']}"
+        output_file_path = os.path.join(org_folder_path, f"{model_name}_{file_suffix}.json")
+
+        with open(output_file_path, 'w', newline="\n") as outfile:
+            json.dump(json_output, outfile, indent=4)
+
+        print(f"File {filename} processed and saved to {output_file_path}")

preprocess_models_output_old.py

@@ -0,0 +1,201 @@
+import json
+import os
+import re
+
+def safe_float(value):
+    """Convert a value to float safely. Returns None if conversion fails."""
+    try:
+        return float(value)
+    except ValueError:
+        return None
+
+
+def calculate_task_metrics(task_info):
+    """Calculate average accuracy, best prompt, and CPS for a task."""
+    accuracies = [prompt['value'] for prompt in task_info['prompts'] if prompt['value'] is not None]
+
+    if not accuracies:
+        return None
+
+    task_info['average_accuracy'] = sum(accuracies) / len(accuracies)
+    best_prompt_data = max(task_info['prompts'], key=lambda x: x['value'])
+    task_info['best_prompt'] = best_prompt_data['value']
+    task_info['prompt_id'] = best_prompt_data['prompt']
+
+    # Calculate CPS
+    avg_acc = task_info['average_accuracy']
+    best_acc = task_info['best_prompt']
+    task_info['CPS'] = (1 - (best_acc - avg_acc) / 100) * best_acc
+
+
+def extract_data_from_file(file_path):
+    """Extract task and prompt data from the given file."""
+    with open(file_path, 'r') as file:
+        lines = file.readlines()
+
+    tasks_data = {}
+    current_task = None
+
+    for line in lines:
+        line = line.strip()
+
+        # Skip irrelevant lines
+        if not line:
+            continue
+
+
+        if line.startswith("|         Tasks"):
+            continue
+
+        if line.startswith("hf (pretrained="):
+
+            # Estrai la parte dopo "pretrained="
+            start = line.find("pretrained=") + len("pretrained=")
+            end = line.find(",", start)  # Trova la virgola successiva
+            # Estrai la stringa desiderata
+            pretrained_model = line[start:end]
+
+            # Estrarre num_fewshot
+            num_fewshot_match = re.search(r"num_fewshot:\s*([\w\d]+)", line)
+            num_fewshot = num_fewshot_match.group(1) if num_fewshot_match else None
+
+            # Estrarre batch_size
+            batch_size_match = re.search(r"batch_size:\s*(\d+)", line)
+            batch_size = int(batch_size_match.group(1)) if batch_size_match else None
+
+            continue
+
+        columns = line.split('|')
+        if len(columns) != 11:
+            continue
+
+        task_name = columns[1]
+        metric = columns[5].strip()
+        value = safe_float(columns[7])
+        stderr = safe_float(columns[9])
+
+        if metric == "acc_norm":
+            continue
+
+        # Identify task and prompts
+        if task_name.startswith(" - "):
+            task_name = task_name[3:].strip()
+            current_task = task_name
+            tasks_data.setdefault(current_task,
+                                  {'prompts': [], 'average_accuracy': 0, 'best_prompt': None, 'prompt_id': None,
+                                   'CPS': None})
+
+        elif task_name.startswith("  - ") and current_task:
+            prompt_name = task_name[4:].strip()
+            prompt_data = {'prompt': prompt_name, 'metric': metric, 'value': value * 100,
+                           'stderr': stderr}
+            tasks_data[current_task]['prompts'].append(prompt_data)
+
+    # Special handling for evalita NER
+    if "evalita NER" in tasks_data:
+        task_info = tasks_data["evalita NER"]
+        weight_map = {"ADG prompt-1": 521, "ADG prompt-2": 521, "FIC prompt-1": 1517, "FIC prompt-2": 1517,
+                      "WN prompt-1": 2088, "WN prompt-2": 2088}
+
+        weighted_values = {"prompt-1": 0, "prompt-2": 0}
+        total_weights = sum(weight_map.values())
+
+        for prompt in task_info['prompts']:
+            if prompt['prompt'] in weight_map:
+                if "prompt-1" in prompt['prompt']:
+                    weighted_values["prompt-1"] += weight_map[prompt['prompt']] * prompt['value']
+                elif "prompt-2" in prompt['prompt']:
+                    weighted_values["prompt-2"] += weight_map[prompt['prompt']] * prompt['value']
+
+        task_info['prompts'] = [
+            {"prompt": "prompt-1", "metric": "acc", "value": weighted_values["prompt-1"] / total_weights,
+             'stderr': None},
+            {"prompt": "prompt-2", "metric": "acc", "value": weighted_values["prompt-2"] / total_weights,
+             'stderr': None}]
+
+    # Calculate metrics for each task
+    for task_info in tasks_data.values():
+        calculate_task_metrics(task_info)
+
+    # Calculate average CPS
+    tasks_with_cps = [task['CPS'] for task in tasks_data.values() if task['CPS'] is not None]
+    average_CPS = sum(tasks_with_cps) / len(tasks_with_cps) if tasks_with_cps else 0
+
+    config = {
+        "model_name": pretrained_model,
+        "num_fewshot": num_fewshot,
+        "batch_size": batch_size
+    }
+
+    return {'average_CPS': average_CPS, 'config': config, 'tasks': tasks_data}
+
+
+# Example usage
+#file_path = '../evalita_llm_results/models_output/slurm-7769.out'
+#json_output = extract_data_from_file(file_path)
+#print(json_output)
+
+
+# Directory da cui leggere i file .out
+directory_in_path = '../evalita_llm_models_output/'
+directory_out_results_path = '../evalita_llm_results/'
+directory_out_requests_path = '../evalita_llm_requests/'
+
+# Itera sui file nella directory
+for filename in os.listdir(directory_in_path):
+    if filename.endswith('.out'):
+        # Costruisci il percorso completo del file
+        file_path = os.path.join(directory_in_path, filename)
+
+        # Esegui la funzione extract_data_from_file
+        json_output = extract_data_from_file(file_path)
+
+        # Estrai model_org_name e model_name da model_name
+        model_org_name, model_name = json_output['config']['model_name'].split('/')
+
+
+
+
+
+
+        # Percorso del file JSON di configurazione in ../evalita_llm_requests2/
+        config_file_path = os.path.join(directory_out_requests_path, model_org_name, f"{model_name}.json")
+
+        # Se il file esiste, caricalo e aggiorna il dizionario config
+        if os.path.exists(config_file_path):
+            with open(config_file_path, 'r', encoding='utf-8') as config_file:
+                additional_config = json.load(config_file)
+
+            # Aggiorna la configurazione con i nuovi dati
+            json_output['config'].update(additional_config)
+
+
+
+
+        # Crea il percorso della cartella per model_org_name
+        org_folder_path = os.path.join(directory_out_results_path, model_org_name)
+        os.makedirs(org_folder_path, exist_ok=True)  # Crea la cartella se non esiste
+
+        # Crea il percorso completo del file JSON
+        file_suffix = f"{json_output['config']['num_fewshot']}"
+        output_file_path = os.path.join(org_folder_path, f"{model_name}_{file_suffix}.json")
+
+        # Salva il JSON in un file con ritorni a capo compatibili con Linux
+        with open(output_file_path, 'w', newline="\n") as outfile:
+            json.dump(json_output, outfile, indent=4)
+
+        # Stampa il risultato
+        print(f"File {filename} elaborato e salvato in {output_file_path}")
+
+
+
+
+
+
+
+
+
+
+
+
+

pyproject.toml

@@ -0,0 +1,13 @@
+[tool.ruff]
+# Enable pycodestyle (`E`) and Pyflakes (`F`) codes by default.
+select = ["E", "F"]
+ignore = ["E501"] # line too long (black is taking care of this)
+line-length = 119
+fixable = ["A", "B", "C", "D", "E", "F", "G", "I", "N", "Q", "S", "T", "W", "ANN", "ARG", "BLE", "COM", "DJ", "DTZ", "EM", "ERA", "EXE", "FBT", "ICN", "INP", "ISC", "NPY", "PD", "PGH", "PIE", "PL", "PT", "PTH", "PYI", "RET", "RSE", "RUF", "SIM", "SLF", "TCH", "TID", "TRY", "UP", "YTT"]
+
+[tool.isort]
+profile = "black"
+line_length = 119
+
+[tool.black]
+line-length = 119

requirements.txt

@@ -0,0 +1,16 @@
+APScheduler
+black
+datasets
+gradio
+gradio[oauth]
+gradio_leaderboard==0.0.13
+gradio_client
+huggingface-hub>=0.18.0
+matplotlib
+numpy
+pandas
+python-dateutil
+tqdm
+transformers
+tokenizers>=0.15.0
+sentencepiece

run_instructions.txt

@@ -0,0 +1,46 @@
+Model Evaluation and Leaderboard
+
+1) Model Evaluation
+Before integrating a model into the leaderboard, it must first be evaluated using the lm-eval-harness library in both zero-shot and 5-shot configurations.
+
+This can be done with the following command:
+
+lm_eval --model hf --model_args pretrained=google/gemma-3-12b-it \
+  --tasks evalita-mp --device cuda:0 --batch_size 1 --trust_remote_code \
+  --output_path model_output --num_fewshot 5 --
+
+The output generated by the library will include the model's accuracy scores on the benchmark tasks.
+This output is written to the standard output and should be saved in a txt file (e.g., slurm-8368.out), which needs to be placed in the
+ evalita_llm_models_output LOCAL directory for further processing. Examples of such files can be found in: https://huggingface.co/datasets/evalitahf/evalita_llm_models_output/
+
+2) Extracting Model Metadata
+To display model details on the leaderboard (e.g., organization/group, model name, and parameter count), metadata must be retrieved from Hugging Face.
+
+This can be done by running:
+
+python get_model_info.py
+
+This script processes the evaluation files from Step 1 and saves each model's metadata in a JSON file within the evalita_llm_requests LOCAL directory.
+
+3) Generating Leaderboard Submission File
+The leaderboard requires a structured file containing each model’s metadata along with its benchmark accuracy scores.
+
+To generate this file, run:
+
+python preprocess_model_output.py
+
+This script combines the accuracy results from Step 1 with the metadata from Step 2 and outputs a JSON file for each kind of model in the evalita_llm_results LOCAL directory.
+Examples of these files are in https://huggingface.co/datasets/evalitahf/evalita_llm_results
+
+4) Updating the Hugging Face Repository
+A commit and push of the following three directories from the local disk to HuggingFace is required, in order to update the evalita_llm_results repository with the newly generated files from Step 3:
+evalita_llm_models_output, evalita_llm_requests and evalita_llm_results
+
+5) Running the Leaderboard Application
+To test the leaderboard locally, run the following command in your terminal and open your browser at the indicated address:
+
+python app.py
+
+On Hugging Face, the leaderboard can be started or stopped directly from the graphical interface, so running this command is only necessary when working locally.
+
+

src/.ipynb_checkpoints/about-checkpoint.py

@@ -0,0 +1,188 @@
+from dataclasses import dataclass
+from enum import Enum
+
+@dataclass
+class Task:
+    benchmark: str
+    metric: str
+    metric_type: str
+    col_name: str
+
+# Select your tasks here
+# ---------------------------------------------------
+class Tasks(Enum):
+    # task_key in the json file, metric_key in the json file, name to display in the leaderboard
+
+    task1 = Task("text-entailment_1", "acc", "CPS", "TE")
+    task2 = Task("text-entailment_2", "acc", "average_accuracy", "TE Prompt Average")
+    task3 = Task("text-entailment_3", "acc", "best_prompt", "TE Best Prompt")
+    task4 = Task("text-entailment_4", "acc", "prompt_id", "TE Best Prompt Id")
+
+    task5 = Task("sentiment-analysis_1", "acc", "CPS", "SA")
+    task6 = Task("sentiment-analysis_2", "acc", "average_accuracy", "SA Prompt Average")
+    task7 = Task("sentiment-analysis_3", "acc", "best_prompt", "SA Best Prompt")
+    task8 = Task("sentiment-analysis_4", "acc", "prompt_id", "SA Best Prompt Id")
+
+    task9 = Task("hate-speech-detection_1", "acc", "CPS", "HS")
+    task10 = Task("hate-speech-detection_2", "acc", "average_accuracy", "HS Prompt Average")
+    task11 = Task("hate-speech-detection_3", "acc", "best_prompt", "HS Best Prompt")
+    task12 = Task("hate-speech-detection_4", "acc", "prompt_id", "HS Best Prompt Id")
+
+    task13 = Task("admission-test_1", "acc", "CPS", "AT")
+    task14 = Task("admission-test_2", "acc", "average_accuracy", "AT Prompt Average")
+    task15 = Task("admission-test_3", "acc", "best_prompt", "AT Best Prompt")
+    task16 = Task("admission-test_4", "acc", "prompt_id", "AT Best Prompt Id")
+
+    task17 = Task("word-in-context_1", "acc", "CPS", "WIC")
+    task18 = Task("word-in-context_2", "acc", "average_accuracy", "WIC Prompt Average")
+    task19 = Task("word-in-context_3", "acc", "best_prompt", "WIC Best Prompt")
+    task20 = Task("word-in-context_4", "acc", "prompt_id", "WIC Best Prompt Id")
+
+    task21 = Task("faq_1", "acc", "CPS", "FAQ")
+    task22 = Task("faq_2", "acc", "average_accuracy", "FAQ Prompt Average")
+    task23 = Task("faq_3", "acc", "best_prompt", "FAQ Best Prompt")
+    task24 = Task("faq_4", "acc", "prompt_id", "FAQ Best Prompt Id")
+
+    task25 = Task("lexical-substitution_1", "acc", "CPS", "LS")
+    task26 = Task("lexical-substitution_2", "acc", "average_accuracy", "LS Prompt Average")
+    task27 = Task("lexical-substitution_3", "acc", "best_prompt", "LS Best Prompt")
+    task28 = Task("lexical-substitution_4", "acc", "prompt_id", "LS Best Prompt Id")
+
+    task29 = Task("summarization-fanpage_1", "acc", "CPS", "SU")
+    task30 = Task("summarization-fanpage_2", "acc", "average_accuracy", "SU Prompt Average")
+    task31 = Task("summarization-fanpage_3", "acc", "best_prompt", "SU Best Prompt")
+    task32 = Task("summarization-fanpage_4", "acc", "prompt_id", "SU Best Prompt Id")
+
+    task33 = Task("evalita NER_1", "acc", "CPS", "NER")
+    task34 = Task("evalita NER_2", "acc", "average_accuracy", "NER Prompt Average")
+    task35 = Task("evalita NER_3", "acc", "best_prompt", "NER Best Prompt")
+    task36 = Task("evalita NER_4", "acc", "prompt_id", "NER Best Prompt Id")
+
+    task37 = Task("relation-extraction_1", "acc", "CPS", "REL")
+    task38 = Task("relation-extraction_2", "acc", "average_accuracy", "REL Prompt Average")
+    task39 = Task("relation-extraction_3", "acc", "best_prompt", "REL Best Prompt")
+    task40 = Task("relation-extraction_4", "acc", "prompt_id", "REL Best Prompt Id")
+
+    '''
+    task0 = Task("TextualEntailment", "acc", "Textual Entailment")
+    task1 = Task("TextualEntailment_best", "acc", "TextualEntailment Best")
+    task2 = Task("Sentiment Analysis", "acc", "Sentiment Analysis")
+    task3 = Task("Sentiment Analysis_best", "acc", "Sentiment Analysis_best")
+    task4 = Task("Hate Speech", "acc", "Hate Speech")
+    task5 = Task("Hate Speech_best", "acc", "Hate Speech_best")
+    task6 = Task("Admission Test", "acc", "Admission Test")
+    task7 = Task("Admission Test_best", "acc", "Admission Test_best")
+    task8 = Task("Word in Context", "acc", "Word in Context")
+    task9 = Task("Word in Context_best", "acc", "Word in Context_best")
+    task10 = Task("FAQ", "acc", "FAQ")
+    task11 = Task("FAQ_best", "acc", "FAQ_best")
+    task12 = Task("Lexical Substitution", "acc", "Lexical Substitution")
+    task13 = Task("Lexical Substitution_best", "acc", "Lexical Substitution_best")
+    task14 = Task("Summarization", "acc", "Summarization")
+    task15 = Task("Summarization_best", "acc", "Summarization_best")
+    task16 = Task("NER", "acc", "NER")
+    task17 = Task("NER_best", "acc", "NER_best")
+    task18 = Task("REL", "acc", "REL")
+    task19 = Task("REL_best", "acc", "REL_best")
+    '''
+
+# Your leaderboard name
+TITLE = """<h1 align="center" id="space-title">🚀 EVALITA-LLM Leaderboard 🚀</h1>"""
+
+# What does your leaderboard evaluate?
+INTRODUCTION_TEXT = """
+Evalita-LLM is a benchmark designed to evaluate Large Language Models (LLMs) on Italian tasks. The distinguishing features of Evalita-LLM are the following: (i) **all tasks are native Italian**, avoiding translation issues and potential cultural biases; (ii) the benchmark includes **generative** tasks, enabling more natural interaction with LLMs; (iii) **all tasks are evaluated against multiple prompts**, this way mitigating the model sensitivity to specific prompts and allowing a fairer evaluation.
+ 
+**<small>Multiple-choice tasks:</small>** <small> 📊TE (Textual Entailment), 😃SA (Sentiment Analysis), ⚠️HS (Hate Speech Detection), 🏥AT (Admission Test), 🔤WIC (Word in Context), ❓FAQ (Frequently Asked Questions) </small><br>
+**<small>Generative tasks:</small>** <small>🔄LS (Lexical Substitution), 📝SU (Summarization), 🏷️NER (Named Entity Recognition), 🔗REL (Relation Extraction) </small>
+"""
+
+# Which evaluations are you running? how can people reproduce what you have?
+LLM_BENCHMARKS_TEXT = f"""
+### Groups
+
+- `evalita-mp`: All tasks (perplexity and non-perplexity based).
+- `evalita-mp_gen`: Only generative tasks.
+- `evalita-mp_mc`: Only multiple-choice tasks.
+
+#### Tasks
+
+The following Evalita-LLM tasks can also be evaluated in isolation:
+ - `evalita-mp_te`: Textual Entailment (TE)
+ - `evalita-mp_sa`: Sentiment Analysis (SA)
+ - `evalita-mp_wic`: Word in Context (WIC)
+ - `evalita-mp_hs`: Hate Speech Detection (HS)
+ - `evalita-mp_at`: Admission Tests (AT)
+ - `evalita-mp_faq`: Frequently Asked Questions & Question Answering (FAQ) 
+ - `evalita-mp_sum_fp`:  Summarization (SU)
+ - `evalita-mp_ls`: Lexical Substitution LS)
+ - `evalita-mp_ner_group`: Named Entity Recognition (NER)
+ - `evalita-mp_re`: Relation Extraction (REL)
+
+
+### Usage
+
+```bash
+
+lm_eval --model hf --model_args pretrained=meta-llama/Llama-2-7b-hf --tasks evalita-mp --device cuda:0 --batch_size 1 
+```
+
+<!-- 
+### Checklist
+
+* [x] Is the task an existing benchmark in the literature?
+ * [x] Have you referenced the original paper that introduced the task?
+ * [x] If yes, does the original paper provide a reference implementation?
+   * [x] Yes, original implementation contributed by author of the benchmark
+
+If other tasks on this dataset are already supported:
+* [x] Is the "Main" variant of this task clearly denoted?
+* [x] Have you provided a short sentence in a README on what each new variant adds / evaluates?
+* [x] Have you noted which, if any, published evaluation setups are matched by this variant?
+-->
+
+
+"""
+
+EVALUATION_QUEUE_TEXT = """
+## Some good practices before submitting a model
+
+### 1) Make sure you can load your model and tokenizer using AutoClasses:
+```python
+from transformers import AutoConfig, AutoModel, AutoTokenizer
+config = AutoConfig.from_pretrained("your model name", revision=revision)
+model = AutoModel.from_pretrained("your model name", revision=revision)
+tokenizer = AutoTokenizer.from_pretrained("your model name", revision=revision)
+```
+If this step fails, follow the error messages to debug your model before submitting it. It's likely your model has been improperly uploaded.
+
+Note: make sure your model is public!
+Note: if your model needs `use_remote_code=True`, we do not support this option yet but we are working on adding it, stay posted!
+
+### 2) Convert your model weights to [safetensors](https://huggingface.co/docs/safetensors/index)
+It's a new format for storing weights which is safer and faster to load and use. It will also allow us to add the number of parameters of your model to the `Extended Viewer`!
+
+### 3) Make sure your model has an open license!
+This is a leaderboard for Open LLMs, and we'd love for as many people as possible to know they can use your model 🤗
+
+### 4) Fill up your model card
+When we add extra information about models to the leaderboard, it will be automatically taken from the model card
+
+## In case of model failure
+If your model is displayed in the `FAILED` category, its execution stopped.
+Make sure you have followed the above steps first.
+If everything is done, check you can launch the EleutherAIHarness on your model locally, using the above command without modifications (you can add `--limit` to limit the number of examples per task).
+"""
+
+CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results"
+CITATION_BUTTON_TEXT = r"""
+@misc{magnini2025evalitallmbenchmarkinglargelanguage,
+     title={Evalita-LLM: Benchmarking Large Language Models on Italian}, 
+     author={Bernardo Magnini and Roberto Zanoli and Michele Resta and Martin Cimmino and Paolo Albano and Marco Madeddu and Viviana Patti},
+     year={2025},
+     eprint={2502.02289},
+     archivePrefix={arXiv},
+     primaryClass={cs.CL},
+     url={https://arxiv.org/abs/2502.02289}, 
+}
+"""

src/.ipynb_checkpoints/envs-checkpoint.py

@@ -0,0 +1,30 @@
+import os
+
+from huggingface_hub import HfApi
+
+# Info to change for your repository
+# ----------------------------------
+TOKEN = os.environ.get("HF_TOKEN") # A read/write token for your org
+
+#OWNER = "giux78" # Change to your org - don't forget to create a results and request dataset, with the correct format!
+OWNER = "evalitahf"
+# ----------------------------------
+
+#REPO_ID = f"{OWNER}/leaderboard-evalita"
+#QUEUE_REPO = f"{OWNER}/evalita-requests"
+#RESULTS_REPO = f"{OWNER}/evalita-results"
+
+REPO_ID = f"{OWNER}/evalita_llm_leaderboard"
+QUEUE_REPO = f"{OWNER}/evalita_llm_requests"
+RESULTS_REPO = f"{OWNER}/evalita_llm_results"
+
+# If you setup a cache later, just change HF_HOME
+CACHE_PATH=os.getenv("HF_HOME", ".")
+
+# Local caches
+EVAL_REQUESTS_PATH = os.path.join(CACHE_PATH, "eval-queue")
+EVAL_RESULTS_PATH = os.path.join(CACHE_PATH, "eval-results")
+EVAL_REQUESTS_PATH_BACKEND = os.path.join(CACHE_PATH, "eval-queue-bk")
+EVAL_RESULTS_PATH_BACKEND = os.path.join(CACHE_PATH, "eval-results-bk")
+
+API = HfApi(token=TOKEN)

src/.ipynb_checkpoints/populate-checkpoint.py

@@ -0,0 +1,58 @@
+import json
+import os
+
+import pandas as pd
+
+from src.display.formatting import has_no_nan_values, make_clickable_model
+from src.display.utils import AutoEvalColumn, EvalQueueColumn
+from src.leaderboard.read_evals import get_raw_eval_results
+
+
+def get_leaderboard_df(results_path: str, requests_path: str, cols: list, benchmark_cols: list) -> pd.DataFrame:
+    """Creates a dataframe from all the individual experiment results"""
+    raw_data = get_raw_eval_results(results_path, requests_path)
+    all_data_json = [v.to_dict() for v in raw_data]
+
+    df = pd.DataFrame.from_records(all_data_json)
+    df = df.sort_values(by=[AutoEvalColumn.average.name], ascending=False)
+    df = df[cols].round(decimals=2)
+
+    # filter out if any of the benchmarks have not been produced
+    df = df[has_no_nan_values(df, benchmark_cols)]
+    return df
+
+
+def get_evaluation_queue_df(save_path: str, cols: list) -> list[pd.DataFrame]:
+    """Creates the different dataframes for the evaluation queues requestes"""
+    entries = [entry for entry in os.listdir(save_path) if not entry.startswith(".")]
+    all_evals = []
+
+    for entry in entries:
+        if ".json" in entry:
+            file_path = os.path.join(save_path, entry)
+            with open(file_path) as fp:
+                data = json.load(fp)
+
+            data[EvalQueueColumn.model.name] = make_clickable_model(data["model"])
+            data[EvalQueueColumn.revision.name] = data.get("revision", "main")
+
+            all_evals.append(data)
+        elif ".md" not in entry:
+            # this is a folder
+            sub_entries = [e for e in os.listdir(f"{save_path}/{entry}") if os.path.isfile(e) and not e.startswith(".")]
+            for sub_entry in sub_entries:
+                file_path = os.path.join(save_path, entry, sub_entry)
+                with open(file_path) as fp:
+                    data = json.load(fp)
+
+                data[EvalQueueColumn.model.name] = make_clickable_model(data["model"])
+                data[EvalQueueColumn.revision.name] = data.get("revision", "main")
+                all_evals.append(data)
+
+    pending_list = [e for e in all_evals if e["status"] in ["PENDING", "RERUN"]]
+    running_list = [e for e in all_evals if e["status"] == "RUNNING"]
+    finished_list = [e for e in all_evals if e["status"].startswith("FINISHED") or e["status"] == "PENDING_NEW_EVAL"]
+    df_pending = pd.DataFrame.from_records(pending_list, columns=cols)
+    df_running = pd.DataFrame.from_records(running_list, columns=cols)
+    df_finished = pd.DataFrame.from_records(finished_list, columns=cols)
+    return df_finished[cols], df_running[cols], df_pending[cols]

src/.ipynb_checkpoints/tasks-checkpoint.py

@@ -0,0 +1,183 @@
+from dataclasses import dataclass
+from enum import Enum
+
+@dataclass
+class Task:
+    benchmark: str
+    # metric: str
+    accuracy: str
+    col_name: str
+
+NUM_FEWSHOT = 0  # Change with your few shot
+# ---------------------------------------------------
+
+# Your leaderboard name
+TITLE = """<h1 align="center" id="space-title">🚀 EVALITA-LLM Leaderboard 🚀</h1>"""
+
+# What does your leaderboard evaluate?
+INTRODUCTION_TEXT = """
+Evalita-LLM is a benchmark designed to evaluate Large Language Models (LLMs) on Italian tasks. The distinguishing features of Evalita-LLM are the following: (i) all tasks are native Italian, avoiding translation issues and potential cultural biases; (ii) the benchmark includes generative tasks, enabling more natural interaction with LLMs; (iii) all tasks are evaluated against multiple prompts, this way mitigating the model sensitivity to specific prompts and allowing a fairer evaluation.
+"""
+
+#MEASURE_DESCRIPTION = "Combined Performance = (1 - (Best_Prompt - Prompt_Average) / 100) * Best_Prompt. Prompt Average = accuracy averaged over the six prompts. Best Prompt = accuracy of the best prompt. Prompt ID = ID of the best prompt (see legend above)"
+MEASURE_DESCRIPTION = "<small>**Combined Performance** = (1 - (**Best Prompt** - **Prompt Average**) / 100) * **Best Prompt**. **Prompt Average** = accuracy averaged over the assessed prompts. **Best Prompt** = accuracy of the best prompt. **Prompt ID** = ID of the best prompt (see legend above).</small>"
+#MEASURE_DESCRIPTION = "<small>**Combined Performance** = (1 - (**Best Prompt** - **Prompt Average**) / 100) * **Best Prompt**. **Prompt Average** = avg. accuracy over prompts. **Best Prompt** = accuracy of best prompt. **Prompt ID** = ID of the best prompt (see legend above).</small>"
+
+# Tasks Descriptions
+TE_DESCRIPTION = """### Textual Entailment (TE) --- *Multiple-choice task*
+    The input are two sentences: the text (T) and the hypothesis (H). The model  has to determine whether the meaning of the hypothesis is logically entailed by the text.
+
+| #   | Prompt | Answer Choices |
+|-----|------------|--------------|
+| 1   | La frase: '{{text1}}' implica logicamente che la frase: '{{text2}}' sia vera? | ["Sì", "No"] |
+| 2   | Devi risolvere un compito di inferenza semantica. La frase: '{{text1}}' implica logicamente che la frase: '{{text2}}' sia vera? | ["Sì", "No"] |
+| 3   | La frase: '{{text1}}' implica logicamente che la frase: '{{text2}}' sia vera?\\nA: Sì\\nB: No\\nRisposta: | ["A", "B"] |
+| 4   | Devi risolvere un compito di inferenza semantica. La frase: '{{text1}}' implica logicamente che la frase: '{{text2}}' sia vera?\\nA: Sì\\nB: No\\nRisposta: | ["A", "B"] |
+| 5   | Frase 1: '{{text1}}' Frase 2: '{{text2}}' | ["La frase 1 implica logicamente che la frase 2 sia vera", "La frase 1 non implica logicamente che la frase 2 sia vera"] |
+| 6   | Devi risolvere un compito di inferenza semantica. Frase 1: '{{text1}}' Frase 2: '{{text2}}' | ["La frase 1 implica logicamente che la frase 2 sia vera", "La frase 1 non implica logicamente che la frase 2 sia vera"] |
+
+<small>**Combined Performance** = (1 - (**Best Prompt** - **Prompt Average**) / 100) * **Best Prompt**. **Prompt Average** = accuracy averaged over the 6 prompts. **Best Prompt** = accuracy of the best prompt. **Prompt ID** = ID of the best prompt (see legend above). </small>
+
+"""
+
+SA_DESCRIPTION = """### Sentiment Analysis (SA) --- *Multiple-choice task*
+    The input is a tweet. The model has to determine the sentiment polarity of the text, categorizing it into one of four classes: positive, negative, neutral, or mixed.
+
+| #   | Prompt                                                                                       | Answer Choices               |
+|-----|--------------------------------------------------------------------------------|-----------------------------|
+| 1   | Qual è il sentiment espresso nel seguente tweet: '{{text}}'?                     | ["Positivo", "Negativo", "Neutro", "Misto"] |
+| 2   | Devi svolgere un compito di analisi del sentiment. Qual è il sentiment espresso nel seguente tweet: '{{text}}'? | ["Positivo", "Negativo", "Neutro", "Misto"] |
+| 3   | Qual è il sentiment espresso nel seguente tweet: '{{text}}'?\\nA: Positivo\\nB: Negativo\\nC: Neutro\\nD: Misto\\nRisposta: | ["A", "B", "C", "D"] |
+| 4   | Devi svolgere un compito di analisi del sentiment. Qual è il sentiment espresso nel seguente tweet: '{{text}}'?\\nA: Positivo\\nB: Negativo\\nC: Neutro\\nD: \\nRisposta: | ["A", "B", "C", "D"] |
+| 5   | Il seguente tweet: '{{text}}' esprime un sentiment                                | ["Positivo", "Negativo", "Neutro", "Misto"]  |
+| 6   | Devi svolgere un compito di analisi del sentiment. Il seguente tweet: '{{text}}' esprime un sentiment | ["Positivo", "Negativo", "Neutro", "Misto"] |
+
+<small>**Combined Performance** = (1 - (**Best Prompt** - **Prompt Average**) / 100) * **Best Prompt**. **Prompt Average** = F1-macro averaged over the 6 prompts. **Best Prompt** = F1-macro of the best prompt. **Prompt ID** = ID of the best prompt (see legend above). </small>
+
+"""
+
+HS_DESCRIPTION = """### Hate Speech (HS) --- *Multiple-choice task*
+    The input is a tweet. The model has to determine whether the text contains hateful content directed towards marginalized or minority groups. The output is a binary classification: hateful or not hateful.
+
+| #   | Prompt                                                                                       | Answer Choices                                   |
+|-----|--------------------------------------------------------------------------------|-------------------------------------------------|
+| 1   | C'è incitamento all'odio nel seguente tweet: '{{full_text}}'?                     | ["Falso", "Vero"] |
+| 2   | Devi svolgere un compito di identificazione di incitamento all'odio. C'è incitamento all'odio nel seguente tweet: '{{full_text}}'? | ["Falso", "Vero"] |
+| 3   | C'è incitamento all'odio nel seguente tweet: '{{full_text}}'?\\nA: \\nB: Falso\\nRisposta: | ["B", "A"]                                      |
+| 4   | Devi svolgere un compito di identificazione di incitamento all'odio. C'è incitamento all'odio nel seguente tweet: '{{full_text}}'?\\nA: \\nB: \\nRisposta: | ["B", "A"]                                      |
+| 5   | Il tweet: '{{full_text}}'                                                      | ["non contiene incitamento all'odio", "contiene incitamento all'odio"] |
+| 6   | Devi svolgere un compito di identificazione di incitamento all'odio. Il tweet: '{{full_text}}' | ["non contiene incitamento all'odio", "contiene incitamento all'odio"] |
+
+<small>**Combined Performance** = (1 - (**Best Prompt** - **Prompt Average**) / 100) * **Best Prompt**. **Prompt Average** = F1-micro averaged over the 6 prompts. **Best Prompt** = F1-micro of the best prompt. **Prompt ID** = ID of the best prompt (see legend above). </small>
+
+"""
+
+AT_DESCRIPTION = """### Admission Tests (AT) --- *Multiple-choice task*
+    The input is a multiple-choice question with five options (A-E) from Italian medical specialty entrance exams, and the model must identify the correct answer.
+
+| #   | Prompt                                                                                       | Answer Choices               |
+|-----|--------------------------------------------------------------------------------|-----------------------------|
+| 1   | Dato il seguente quesito di medicina: '{{Question}}' qual è la risposta corretta? | ["A", "B", "C", "D", "E"]   |
+| 2   | Devi risolvere un compito di risposte a domande. Dato il seguente quesito di medicina: '{{Question}}' qual è la risposta corretta? | ["A", "B", "C", "D", "E"]   |
+| 3   | Dato il seguente quesito di medicina: '{{Question}}' qual è la risposta corretta?\\nA: {{A}}\\nB: {{B}}\\nC: {{C}}\\nD: {{D}}\\nE: {{E}}\\nRisposta: | ["A", "B", "C", "D", "E"]   |
+| 4   | Devi risolvere un compito a scelta multipla. Dato il seguente caso clinico: '{{background}}', qual è la risposta corretta alla domanda: '{{domanda}}'?\\nA: {{A}}\\nB: {{B}}\\nC: {{C}}\\nD: {{D}}\\nE: {{E}}\\nRisposta:Devi risolvere un compito a scelta multipla. Dato il seguente quesito di medicina: '{{Question}}' qual è la risposta corretta?\\nA: {{A}}\\nB: {{B}}\\nC: {{C}}\\nD: {{D}}\\nE: {{E}}\\nRisposta: | ["A", "B", "C", "D", "E"]   |
+| 5   | Dato il seguente caso clinico: '{{background}}'. La risposta corretta alla domanda: '{{domanda}}' èDato il seguente quesito di medicina '{{Question}}' la risposta corretta è: | ["A", "B", "C", "D", "E"]   |
+| 6   | Devi risolvere un compito di risposte a domande. Dato il seguente quesito di medicina '{{Question}}' la risposta corretta è: | ["A", "B", "C", "D", "E"]   |
+
+<small>**Combined Performance** = (1 - (**Best Prompt** - **Prompt Average**) / 100) * **Best Prompt**. **Prompt Average** = accuracy averaged over the 6 prompts. **Best Prompt** = accuracy of the best prompt. **Prompt ID** = ID of the best prompt (see legend above). </small>
+
+"""
+
+WIC_DESCRIPTION = """### Word in Context (WIC) --- *Multiple-choice task*
+    The input consists of a word (w) and two sentences. The model has to determine whether the word w has the same meaning in both sentences. The output is a binary classification: 1 (same meaning) or 0 (different meaning).
+
+| #   | Prompt                                                                                       | Answer Choices                                   |
+|-----|--------------------------------------------------------------------------------|-------------------------------------------------|
+| 1   | La parola: '{{sentence1[start1:end1]}}' nella frase: '{{sentence1}}' ha lo stesso significato della parola: '{{sentence2[start2:end2]}}' nella frase: '{{sentence2}}'? | ["No", "Sì"]                         |
+| 2   | Devi determinare se una stessa parola usata in due frasi differenti ha lo stesso significato in entrambi i contesti. La parola: '{{sentence1[start1:end1]}}' nella frase: '{{sentence1}}' ha lo stesso significato della parola: '{{sentence2[start2:end2]}}' nella frase: '{{sentence2}}'? | ["No", "Sì"] |
+| 3   | La parola: '{{sentence1[start1:end1]}}' nella frase: '{{sentence1}}' ha lo stesso significato della parola: '{{sentence2[start2:end2]}}' nella frase: '{{sentence2}}'?\\nA: Sì\\nB: No\\nRisposta: | ["B", "A"]                                      |
+| 4   | Devi determinare se una stessa parola usata in due frasi differenti ha lo stesso significato in entrambi i contesti. La parola: '{{sentence1[start1:end1]}}' nella frase: '{{sentence1}}' ha lo stesso significato della parola: '{{sentence2[start2:end2]}}' nella frase: '{{sentence2}}'?\\nA: \\nB: No\\nRisposta: | ["B", "A"]  |
+| 5   | La parola: '{{sentence1[start1:end1]}}' nella frase: '{{sentence1}}' e la parola: '{{sentence2[start2:end2]}}' nella frase: '{{sentence2}}' | ["non hanno lo stesso significato", "hanno lo stesso significato"] |
+| 6   | Devi determinare se una stessa parola usata in due frasi differenti ha lo stesso significato in entrambi i contesti. La parola: '{{sentence1[start1:end1]}}' nella frase: '{{sentence1}}' e la parola: '{{sentence2[start2:end2]}}' nella frase: '{{sentence2}}' | ["non hanno lo stesso significato", "hanno lo stesso significato"] |
+
+<small>**Combined Performance** = (1 - (**Best Prompt** - **Prompt Average**) / 100) * **Best Prompt**. **Prompt Average** = F1-macro averaged over the 6 prompts. **Best Prompt** = F1-macro of the best prompt. **Prompt ID** = ID of the best prompt (see legend above). </small>
+
+"""
+
+FAQ_DESCRIPTION = """### Frequently Asked Questions & Question Answering (FAQ) --- *Multiple-choice task*
+    The input is a user query regarding the water supply service. The model must identify the correct answer from the 4 available options.
+
+| #   | Prompt                                                                                       | Answer Choices               |
+|-----|--------------------------------------------------------------------------------|-----------------------------|
+| 1   | Rispondi alla seguente domanda: '{{question}}'                                              | {{[A, B, C, D]}}            |
+| 2   | Devi risolvere un compito di risposte a domande. Rispondi alla seguente domanda: '{{question}}' | {{[A, B, C, D]}}            |
+| 3   | Rispondi alla seguente domanda: '{{question}}'\\nA: {{A}}\\nB: {{B}}\\nC: {{C}}\\nD: {{D}}\\nRisposta: | ["A", "B", "C", "D"] |
+| 4   | Devi risolvere un compito a scelta multipla. Rispondi alla seguente domanda: '{{question}}'\\nA: {{A}}\\nB: {{B}}\\nC: {{C}}\\nD: {{D}}\\nRisposta: | ["A", "B", "C", "D"] |
+| 5   | La risposta alla domanda: '{{question}}' è:                                                | {{[A, B, C, D]}}            |
+| 6   | Devi risolvere un compito di risposte a domande. La risposta alla domanda: '{{question}}' è: | {{[A, B, C, D]}}  |
+
+<small>**Combined Performance** = (1 - (**Best Prompt** - **Prompt Average**) / 100) * **Best Prompt**. **Prompt Average** = accuracy averaged over the 6 prompts. **Best Prompt** = accuracy of the best prompt. **Prompt ID** = ID of the best prompt (see legend above). </small>
+
+"""
+
+LS_DESCRIPTION = """### Lexical Substitution (LS) --- *Generative task*
+    The input is a sentence containing a target word (w). The model has to replace the target word w with its most suitable synonyms that are contextually relevant. 
+
+| #   | Prompt                                                                                       |
+|-----|--------------------------------------------------------------------------------|
+| 1   | Trova 10 parole che possono sostituire la parola racchiusa tra i marcatori `<head>` nella seguente frase: '{{context}}', mantenendo lo stesso significato. Elenca i lemmi (forme base) di queste parole, separandoli con una virgola, ad esempio: lemma1, lemma2, lemma3, lemma4, lemma5. Non aggiungere commenti o altro testo. Risposta: |
+| 2   | Devi risolvere un compito di sostituzione lessicale. Trova 10 parole che possono sostituire la parola racchiusa tra i marcatori `<head>` nella seguente frase: '{{context}}', mantenendo lo stesso significato. Elenca i lemmi (forme base) di queste parole, separandoli con una virgola, ad esempio: lemma1, lemma2, lemma3, lemma4, lemma5. Non aggiungere commenti o altro testo. Risposta: |
+
+<small>**Combined Performance** = (1 - (**Best Prompt** - **Prompt Average**) / 100) * **Best Prompt**. **Prompt Average** = F1 averaged over the 2 prompts. **Best Prompt** = F1 of the best prompt. **Prompt ID** = ID of the best prompt (see legend above). </small>
+
+"""
+
+SU_DESCRIPTION = """### Summarization (SUM) --- *Generative task*
+    The input is a news article. The model has to generate a concise summary of the input text, capturing the key information and main points.
+
+| #   | Prompt                                                                                       |
+|-----|--------------------------------------------------------------------------------|
+| 1   | Riassumi il seguente articolo di giornale: '{{source}}'\\nRiassunto:             |
+| 2   | Devi risolvere un compito di sintesi automatica del testo. Riassumi il seguente articolo di giornale: '{{source}}'\\nRiassunto: |
+
+<small>**Combined Performance** = (1 - (**Best Prompt** - **Prompt Average**) / 100) * **Best Prompt**. **Prompt Average** = F1 averaged over the 2 prompts. **Best Prompt** = F1 of the best prompt. **Prompt ID** = ID of the best prompt (see legend above). </small>
+
+"""
+
+NER_DESCRIPTION = """### Named Entity Recognition (NER) --- *Generative task*
+    The input is a sentence. The model has to identify and classify Named Entities into predefined categories such as person, organization, and location.
+
+| #   | Prompt                                                                                       |
+|-----|--------------------------------------------------------------------------------|
+| 1   | Estrai tutte le entità di tipo PER (persona), LOC (luogo) e ORG (organizzazione) dal testo seguente. Riporta ogni entità con il formato: Entità$Tipo, separando ciascuna coppia con ','. Se non ci sono entità da estrarre, rispondi con '&&NOENT&&'.\\nTesto: '{{text}}'\\nEntità: |
+| 2   | Devi svolgere un compito di riconoscimento delle entità nei testi. Estrai tutte le entità di tipo PER (persona), LOC (luogo) e ORG (organizzazione) dal testo seguente. Riporta ogni entità con il formato: Entità$Tipo, separando ciascuna coppia con ','. Se non ci sono entità da estrarre, rispondi con '&&NOENT&&'.\\nTesto: '{{text}}'\\nEntità: |
+
+<small>**Combined Performance** = (1 - (**Best Prompt** - **Prompt Average**) / 100) * **Best Prompt**. **Prompt Average** = F1 averaged over the 2 prompts. **Best Prompt** = F1 of the best prompt. **Prompt ID** = ID of the best prompt (see legend above). </small>
+
+"""
+
+REL_DESCRIPTION = """### Relation Extraction (REL) --- *Generative task*
+    The input is a sentence of a clinical text. The model must identify and extract relationships between laboratory test results (e.g., blood pressure) and the corresponding tests or procedures that generated them (e.g., blood pressure test).
+
+| #   | Prompt                                                                                       |
+|-----|--------------------------------------------------------------------------------|
+| 1   | Dato un documento medico devi estrarre tutte le misurazioni degli esami medici presenti. Riporta ogni relazione nel formato: misurazione$esame, separando ciascuna coppia con '%'. Se non ci sono relazioni da estrarre, rispondi con '&&NOREL&&'.\\nTesto: '{{text}}'\\nRelazioni: |
+| 2   | Devi svolgere un compito di estrazione di relazioni da documenti medici. Dato un documento medico devi estrarre tutte le misurazioni degli esami medici presenti. Riporta ogni relazione nel formato: misurazione$esame, separando ciascuna coppia con '%'. Se non ci sono relazioni da estrarre, rispondi con '&&NOREL&&'.\\nTesto: '{{text}}'\\nRelazioni: |
+
+<small>**Combined Performance** = (1 - (**Best Prompt** - **Prompt Average**) / 100) * **Best Prompt**. **Prompt Average** = F1 averaged over the 2 prompts. **Best Prompt** = F1 of the best prompt. **Prompt ID** = ID of the best prompt (see legend above). </small>
+
+"""
+
+# Create a dictionary to map task names to their descriptions
+TASK_DESCRIPTIONS = {
+    "TE": TE_DESCRIPTION,
+    "SA": SA_DESCRIPTION,
+    "HS": HS_DESCRIPTION,
+    "AT": AT_DESCRIPTION,
+    "WIC": WIC_DESCRIPTION,
+    "FAQ": FAQ_DESCRIPTION,
+    "LS": LS_DESCRIPTION,
+    "SU": SU_DESCRIPTION,
+    "NER": NER_DESCRIPTION,
+    "REL": REL_DESCRIPTION
+}

src/about.py

@@ -0,0 +1,198 @@
+from dataclasses import dataclass
+from enum import Enum
+
+@dataclass
+class Task:
+    benchmark: str
+    metric: str
+    metric_type: str
+    col_name: str
+
+# Select your tasks here
+# ---------------------------------------------------
+class Tasks(Enum):
+    # task_key in the json file, metric_key in the json file, name to display in the leaderboard
+
+    #task1 = Task("text-entailment_1", "acc", "CPS", "TE")
+    #task2 = Task("text-entailment_2", "acc", "average_accuracy", "TE Prompt Average")
+    #task3 = Task("text-entailment_3", "acc", "best_prompt", "TE Best Prompt")
+    #task4 = Task("text-entailment_4", "acc", "prompt_id", "TE Best Prompt Id")
+
+    #task5 = Task("sentiment-analysis_1", "acc", "CPS", "SA")
+    #task6 = Task("sentiment-analysis_2", "acc", "average_accuracy", "SA Prompt Average")
+    #task7 = Task("sentiment-analysis_3", "acc", "best_prompt", "SA Best Prompt")
+    #task8 = Task("sentiment-analysis_4", "acc", "prompt_id", "SA Best Prompt Id")
+
+    #task9 = Task("hate-speech-detection_1", "acc", "CPS", "HS")
+    #task10 = Task("hate-speech-detection_2", "acc", "average_accuracy", "HS Prompt Average")
+    #task11 = Task("hate-speech-detection_3", "acc", "best_prompt", "HS Best Prompt")
+    #task12 = Task("hate-speech-detection_4", "acc", "prompt_id", "HS Best Prompt Id")
+
+    #task13 = Task("admission-test_1", "acc", "CPS", "AT")
+    #task14 = Task("admission-test_2", "acc", "average_accuracy", "AT Prompt Average")
+    #task15 = Task("admission-test_3", "acc", "best_prompt", "AT Best Prompt")
+    #task16 = Task("admission-test_4", "acc", "prompt_id", "AT Best Prompt Id")
+
+    #task17 = Task("word-in-context_1", "acc", "CPS", "WIC")
+    #task18 = Task("word-in-context_2", "acc", "average_accuracy", "WIC Prompt Average")
+    #task19 = Task("word-in-context_3", "acc", "best_prompt", "WIC Best Prompt")
+    #task20 = Task("word-in-context_4", "acc", "prompt_id", "WIC Best Prompt Id")
+
+    #task21 = Task("faq_1", "acc", "CPS", "FAQ")
+    #task22 = Task("faq_2", "acc", "average_accuracy", "FAQ Prompt Average")
+    #task23 = Task("faq_3", "acc", "best_prompt", "FAQ Best Prompt")
+    #task24 = Task("faq_4", "acc", "prompt_id", "FAQ Best Prompt Id")
+
+    #task25 = Task("lexical-substitution_1", "acc", "CPS", "LS")
+    #task26 = Task("lexical-substitution_2", "acc", "average_accuracy", "LS Prompt Average")
+    #task27 = Task("lexical-substitution_3", "acc", "best_prompt", "LS Best Prompt")
+    #task28 = Task("lexical-substitution_4", "acc", "prompt_id", "LS Best Prompt Id")
+
+    #task29 = Task("summarization-fanpage_1", "acc", "CPS", "SU")
+    #task30 = Task("summarization-fanpage_2", "acc", "average_accuracy", "SU Prompt Average")
+    #task31 = Task("summarization-fanpage_3", "acc", "best_prompt", "SU Best Prompt")
+    #task32 = Task("summarization-fanpage_4", "acc", "prompt_id", "SU Best Prompt Id")
+
+    #task33 = Task("NER_1", "acc", "CPS", "NER")
+    #task34 = Task("NER_2", "acc", "average_accuracy", "NER Prompt Average")
+    #task35 = Task("NER_3", "acc", "best_prompt", "NER Best Prompt")
+    #task36 = Task("NER_4", "acc", "prompt_id", "NER Best Prompt Id")
+
+    #task37 = Task("relation-extraction_1", "acc", "CPS", "REL")
+    #task38 = Task("relation-extraction_2", "acc", "average_accuracy", "REL Prompt Average")
+    #task39 = Task("relation-extraction_3", "acc", "best_prompt", "REL Best Prompt")
+    #task40 = Task("relation-extraction_4", "acc", "prompt_id", "REL Best Prompt Id")
+    task1 = Task("RE_1", "acc", "CPS", "REL")
+    task2 = Task("RE_2", "acc", "average_accuracy", "REL Prompt Average")
+    task3 = Task("RE_3", "acc", "best_prompt", "REL Best Prompt")
+    task4 = Task("RE_4", "acc", "prompt_id", "REL Best Prompt Id")
+
+    task5 = Task("NER_1", "acc", "CPS", "NER")
+    task6 = Task("NER_2", "acc", "average_accuracy", "NER Prompt Average")
+    task7 = Task("NER_3", "acc", "best_prompt", "NER Best Prompt")
+    task8 = Task("NER_4", "acc", "prompt_id", "NER Best Prompt Id")  
+    '''
+    task0 = Task("TextualEntailment", "acc", "Textual Entailment")
+    task1 = Task("TextualEntailment_best", "acc", "TextualEntailment Best")
+    task2 = Task("Sentiment Analysis", "acc", "Sentiment Analysis")
+    task3 = Task("Sentiment Analysis_best", "acc", "Sentiment Analysis_best")
+    task4 = Task("Hate Speech", "acc", "Hate Speech")
+    task5 = Task("Hate Speech_best", "acc", "Hate Speech_best")
+    task6 = Task("Admission Test", "acc", "Admission Test")
+    task7 = Task("Admission Test_best", "acc", "Admission Test_best")
+    task8 = Task("Word in Context", "acc", "Word in Context")
+    task9 = Task("Word in Context_best", "acc", "Word in Context_best")
+    task10 = Task("FAQ", "acc", "FAQ")
+    task11 = Task("FAQ_best", "acc", "FAQ_best")
+    task12 = Task("Lexical Substitution", "acc", "Lexical Substitution")
+    task13 = Task("Lexical Substitution_best", "acc", "Lexical Substitution_best")
+    task14 = Task("Summarization", "acc", "Summarization")
+    task15 = Task("Summarization_best", "acc", "Summarization_best")
+    task16 = Task("NER", "acc", "NER")
+    task17 = Task("NER_best", "acc", "NER_best")
+    task18 = Task("REL", "acc", "REL")
+    task19 = Task("REL_best", "acc", "REL_best")
+    '''
+
+# Your leaderboard name
+TITLE = """<h1 align="center" id="space-title">🚀 ECREAM-LLM Leaderboard 🚀</h1>"""
+
+# What does your leaderboard evaluate?
+INTRODUCTION_TEXT = """
+Ecream  ECREAM-LLM is a benchmark designed to evaluate Large Language Models (LLMs) on 6 languges. ..........................................................
+..............................
+................................
+ 
+**<small>Multiple-choice tasks:</small>** <small> 📊TE (Textual Entailment), 😃SA (Sentiment Analysis), ⚠️HS (Hate Speech Detection), 🏥AT (Admission Test), 🔤WIC (Word in Context), ❓FAQ (Frequently Asked Questions) </small><br>
+**<small>Generative tasks:</small>** <small>🔄LS (Lexical Substitution), 📝SU (Summarization), 🏷️NER (Named Entity Recognition), 🔗REL (Relation Extraction) </small>
+"""
+
+# Which evaluations are you running? how can people reproduce what you have?
+LLM_BENCHMARKS_TEXT = f"""
+### Groups
+
+- `evalita-mp`: All tasks (perplexity and non-perplexity based).
+- `evalita-mp_gen`: Only generative tasks.
+- `evalita-mp_mc`: Only multiple-choice tasks.
+
+#### Tasks
+
+The following Evalita-LLM tasks can also be evaluated in isolation:
+ - `evalita-mp_te`: Textual Entailment (TE)
+ - `evalita-mp_sa`: Sentiment Analysis (SA)
+ - `evalita-mp_wic`: Word in Context (WIC)
+ - `evalita-mp_hs`: Hate Speech Detection (HS)
+ - `evalita-mp_at`: Admission Tests (AT)
+ - `evalita-mp_faq`: Frequently Asked Questions & Question Answering (FAQ) 
+ - `evalita-mp_sum_fp`:  Summarization (SU)
+ - `evalita-mp_ls`: Lexical Substitution LS)
+ - `evalita-mp_ner_group`: Named Entity Recognition (NER)
+ - `evalita-mp_re`: Relation Extraction (REL)
+
+
+### Usage
+
+```bash
+
+lm_eval --model hf --model_args pretrained=meta-llama/Llama-2-7b-hf --tasks evalita-mp --device cuda:0 --batch_size 1 
+```
+
+<!-- 
+### Checklist
+
+* [x] Is the task an existing benchmark in the literature?
+ * [x] Have you referenced the original paper that introduced the task?
+ * [x] If yes, does the original paper provide a reference implementation?
+   * [x] Yes, original implementation contributed by author of the benchmark
+
+If other tasks on this dataset are already supported:
+* [x] Is the "Main" variant of this task clearly denoted?
+* [x] Have you provided a short sentence in a README on what each new variant adds / evaluates?
+* [x] Have you noted which, if any, published evaluation setups are matched by this variant?
+-->
+
+
+"""
+
+EVALUATION_QUEUE_TEXT = """
+## Some good practices before submitting a model
+
+### 1) Make sure you can load your model and tokenizer using AutoClasses:
+```python
+from transformers import AutoConfig, AutoModel, AutoTokenizer
+config = AutoConfig.from_pretrained("your model name", revision=revision)
+model = AutoModel.from_pretrained("your model name", revision=revision)
+tokenizer = AutoTokenizer.from_pretrained("your model name", revision=revision)
+```
+If this step fails, follow the error messages to debug your model before submitting it. It's likely your model has been improperly uploaded.
+
+Note: make sure your model is public!
+Note: if your model needs `use_remote_code=True`, we do not support this option yet but we are working on adding it, stay posted!
+
+### 2) Convert your model weights to [safetensors](https://huggingface.co/docs/safetensors/index)
+It's a new format for storing weights which is safer and faster to load and use. It will also allow us to add the number of parameters of your model to the `Extended Viewer`!
+
+### 3) Make sure your model has an open license!
+This is a leaderboard for Open LLMs, and we'd love for as many people as possible to know they can use your model 🤗
+
+### 4) Fill up your model card
+When we add extra information about models to the leaderboard, it will be automatically taken from the model card
+
+## In case of model failure
+If your model is displayed in the `FAILED` category, its execution stopped.
+Make sure you have followed the above steps first.
+If everything is done, check you can launch the EleutherAIHarness on your model locally, using the above command without modifications (you can add `--limit` to limit the number of examples per task).
+"""
+
+CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results"
+CITATION_BUTTON_TEXT = r"""
+@misc{magnini2025evalitallmbenchmarkinglargelanguage,
+     title={Evalita-LLM: Benchmarking Large Language Models on Italian}, 
+     author={Bernardo Magnini and Roberto Zanoli and Michele Resta and Martin Cimmino and Paolo Albano and Marco Madeddu and Viviana Patti},
+     year={2025},
+     eprint={2502.02289},
+     archivePrefix={arXiv},
+     primaryClass={cs.CL},
+     url={https://arxiv.org/abs/2502.02289}, 
+}
+"""

src/display/.ipynb_checkpoints/utils-checkpoint.py

@@ -0,0 +1,160 @@
+from dataclasses import dataclass, make_dataclass
+from enum import Enum
+
+import pandas as pd
+
+from src.about import Tasks
+
+def fields(raw_class):
+    return [v for k, v in raw_class.__dict__.items() if k[:2] != "__" and k[-2:] != "__"]
+
+
+# These classes are for user facing column names,
+# to avoid having to change them all around the code
+# when a modif is needed
+@dataclass
+class ColumnContent:
+    name: str
+    type: str
+    displayed_by_default: bool
+    hidden: bool = False
+    never_hidden: bool = False
+
+## Leaderboard columns
+auto_eval_column_dict = []
+# Init
+#auto_eval_column_dict.append(["model_type_symbol", ColumnContent, ColumnContent("T", "str", True, never_hidden=True)])
+
+auto_eval_column_dict.append(["fewshot_symbol", ColumnContent, ColumnContent("FS", "str", True, never_hidden=True)])
+auto_eval_column_dict.append(["is_5fewshot", ColumnContent, ColumnContent("IS_FS", "bool", True)])
+
+auto_eval_column_dict.append(["model", ColumnContent, ColumnContent("Model", "markdown", True, never_hidden=True)])
+#auto_eval_column_dict.append(["fewshot", ColumnContent, ColumnContent("Few-Shot", "str", True)])
+
+#Scores
+auto_eval_column_dict.append(["average", ColumnContent, ColumnContent("Avg. Combined Performance ⬆️", "number", True)])
+for task in Tasks:
+    auto_eval_column_dict.append([task.name, ColumnContent, ColumnContent(task.value.col_name, "number", True)])
+
+# Model information
+#auto_eval_column_dict.append(["model_type", ColumnContent, ColumnContent("Type", "str", False)])
+auto_eval_column_dict.append(["architecture", ColumnContent, ColumnContent("Architecture", "str", False)])
+auto_eval_column_dict.append(["weight_type", ColumnContent, ColumnContent("Weight type", "str", False, True)])
+#auto_eval_column_dict.append(["precision", ColumnContent, ColumnContent("Precision", "str", False)])
+auto_eval_column_dict.append(["license", ColumnContent, ColumnContent("Hub License", "str", False)])
+auto_eval_column_dict.append(["params", ColumnContent, ColumnContent("#Params (B)", "number", False)])
+auto_eval_column_dict.append(["likes", ColumnContent, ColumnContent("Hub ❤️", "number", False)])
+auto_eval_column_dict.append(["still_on_hub", ColumnContent, ColumnContent("Available on the hub", "bool", False)])
+auto_eval_column_dict.append(["revision", ColumnContent, ColumnContent("Model sha", "str", False, False)])
+
+# We use make dataclass to dynamically fill the scores from Tasks
+AutoEvalColumn = make_dataclass("AutoEvalColumn", auto_eval_column_dict, frozen=True)
+
+## For the queue columns in the submission tab
+@dataclass(frozen=True)
+class EvalQueueColumn:  # Queue column
+    model = ColumnContent("model", "markdown", True)
+    revision = ColumnContent("revision", "str", True)
+    private = ColumnContent("private", "bool", True)
+    #precision = ColumnContent("precision", "str", True)
+    weight_type = ColumnContent("weight_type", "str", "Original")
+    status = ColumnContent("status", "str", True)
+
+## All the model information that we might need
+@dataclass
+class ModelDetails:
+    name: str
+    display_name: str = ""
+    symbol: str = "" # emoji
+
+
+class ModelType(Enum):
+    PT = ModelDetails(name="pretrained", symbol="🟢")
+    FT = ModelDetails(name="fine-tuned", symbol="🔶")
+    IFT = ModelDetails(name="instruction-tuned", symbol="⭕")
+    RL = ModelDetails(name="RL-tuned", symbol="🟦")
+    Unknown = ModelDetails(name="", symbol="?")
+
+    def to_str(self, separator=" "):
+        return f"{self.value.symbol}{separator}{self.value.name}"
+
+    @staticmethod
+    def from_str(type):
+        if "fine-tuned" in type or "🔶" in type:
+            return ModelType.FT
+        if "pretrained" in type or "🟢" in type:
+            return ModelType.PT
+        if "RL-tuned" in type or "🟦" in type:
+            return ModelType.RL
+        if "instruction-tuned" in type or "⭕" in type:
+            return ModelType.IFT
+        return ModelType.Unknown
+
+@dataclass
+class FewShotDetails:
+    name: str
+    symbol: str = ""  # emoji
+
+class FewShotType(Enum):
+    ZS = FewShotDetails(name="zero-shot", symbol="0️⃣")
+    FS = FewShotDetails(name="5-few-shot", symbol="5️⃣")
+    Unknown = FewShotDetails(name="unknown", symbol="❓")
+
+    def to_str(self, separator=" "):
+        return f"{self.value.symbol}{separator}{self.value.name}"
+
+    @staticmethod
+    def from_num_fewshot(is_5fewshot):
+        """Determines FewShotType based on num_fewshot."""
+        if is_5fewshot is False:
+            return FewShotType.ZS
+        elif is_5fewshot is True:
+            return FewShotType.FS
+        return FewShotType.Unknown
+
+class WeightType(Enum):
+    Adapter = ModelDetails("Adapter")
+    Original = ModelDetails("Original")
+    Delta = ModelDetails("Delta")
+
+class Precision(Enum):
+    float16 = ModelDetails("float16")
+    bfloat16 = ModelDetails("bfloat16")
+    Unknown = ModelDetails("?")
+
+    def from_str(precision):
+        if precision in ["torch.float16", "float16"]:
+            return Precision.float16
+        if precision in ["torch.bfloat16", "bfloat16"]:
+            return Precision.bfloat16
+        return Precision.Unknown
+
+# Column selection
+COLS = [c.name for c in fields(AutoEvalColumn) if not c.hidden]
+
+EVAL_COLS = [c.name for c in fields(EvalQueueColumn)]
+EVAL_TYPES = [c.type for c in fields(EvalQueueColumn)]
+
+BENCHMARK_COLS = [t.value.col_name for t in Tasks]
+
+'''
+# Nuovi valori per CPS, AVERAGE, BEST, e ID nella tabella
+@dataclass
+class NewColumnContent:
+    name: str
+    type: str
+    displayed_by_default: bool
+    hidden: bool = False
+    never_hidden: bool = False
+'''
+
+'''
+new_column_dict = []
+# Aggiungi CPS, VERAGE, BEST, ID
+new_column_dict.append(["CPS", NewColumnContent, NewColumnContent("CPS", "number", True)])
+new_column_dict.append(["AVERAGE", NewColumnContent, NewColumnContent("Average ⬆️", "number", True)])
+new_column_dict.append(["BEST", NewColumnContent, NewColumnContent("Best Performance", "number", True)])
+new_column_dict.append(["ID", NewColumnContent, NewColumnContent("ID", "str", True)])
+NewColumn = make_dataclass("NewColumn", new_column_dict, frozen=True)
+NEW_COLS = [c.name for c in fields(NewColumn) if not c.hidden]
+'''

src/display/css_html_js.py

@@ -0,0 +1,106 @@
+custom_css = """
+
+.markdown-text {
+    font-size: 16px !important;
+}
+
+#models-to-add-text {
+    font-size: 18px !important;
+}
+
+#citation-button span {
+    font-size: 16px !important;
+}
+
+#citation-button textarea {
+    font-size: 16px !important;
+}
+
+#citation-button > label > button {
+    margin: 6px;
+    transform: scale(1.3);
+}
+
+#leaderboard-table {
+    margin-top: 15px
+}
+
+#leaderboard-table-lite {
+    margin-top: 15px
+}
+
+#search-bar-table-box > div:first-child {
+    background: none;
+    border: none;
+}
+ 
+#search-bar {
+    padding: 0px;
+}
+
+/* Limit the width of the first AutoEvalColumn so that names don't expand too much */
+#leaderboard-table td:nth-child(2),
+#leaderboard-table th:nth-child(2) {
+    max-width: 400px;
+    overflow: auto;
+    white-space: nowrap;
+}
+
+.tab-buttons button {
+    font-size: 20px;
+}
+
+#scale-logo {
+    border-style: none !important;
+    box-shadow: none;
+    display: block;
+    margin-left: auto;
+    margin-right: auto;
+    max-width: 600px;
+}
+
+#scale-logo .download {
+    display: none;
+}
+#filter_type{
+    border: 0;
+    padding-left: 0;
+    padding-top: 0;
+}
+#filter_type label {
+    display: flex;
+}
+#filter_type label > span{
+    margin-top: var(--spacing-lg);
+    margin-right: 0.5em;
+}
+#filter_type label > .wrap{
+    width: 103px;
+}
+#filter_type label > .wrap .wrap-inner{  
+    padding: 2px;
+}
+#filter_type label > .wrap .wrap-inner input{
+    width: 1px
+}
+#filter-columns-type{
+    border:0;
+    padding:0.5;
+}
+#filter-columns-size{
+    border:0;
+    padding:0.5;
+}
+#box-filter > .form{
+    border: 0
+}
+
+"""
+
+get_window_url_params = """
+    function(url_params) {
+        const params = new URLSearchParams(window.location.search);
+        url_params = Object.fromEntries(params);
+        return url_params;
+    }
+    """

src/display/formatting.py

@@ -0,0 +1,27 @@
+def model_hyperlink(link, model_name):
+    return f'<a target="_blank" href="{link}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{model_name}</a>'
+
+
+def make_clickable_model(model_name):
+    link = f"https://huggingface.co/{model_name}"
+    return model_hyperlink(link, model_name)
+
+
+def styled_error(error):
+    return f"<p style='color: red; font-size: 20px; text-align: center;'>{error}</p>"
+
+
+def styled_warning(warn):
+    return f"<p style='color: orange; font-size: 20px; text-align: center;'>{warn}</p>"
+
+
+def styled_message(message):
+    return f"<p style='color: green; font-size: 20px; text-align: center;'>{message}</p>"
+
+
+def has_no_nan_values(df, columns):
+    return df[columns].notna().all(axis=1)
+
+
+def has_nan_values(df, columns):
+    return df[columns].isna().any(axis=1)

src/display/utils.py

@@ -0,0 +1,166 @@
+from dataclasses import dataclass, make_dataclass
+from enum import Enum
+
+import pandas as pd
+
+from src.about import Tasks
+
+def fields(raw_class):
+    return [v for k, v in raw_class.__dict__.items() if k[:2] != "__" and k[-2:] != "__"]
+
+
+# These classes are for user facing column names,
+# to avoid having to change them all around the code
+# when a modif is needed
+@dataclass
+class ColumnContent:
+    name: str
+    type: str
+    displayed_by_default: bool
+    hidden: bool = False
+    never_hidden: bool = False
+
+## Leaderboard columns
+auto_eval_column_dict = []
+# Init
+#auto_eval_column_dict.append(["model_type_symbol", ColumnContent, ColumnContent("T", "str", True, never_hidden=True)])
+
+auto_eval_column_dict.append(["fewshot_symbol", ColumnContent, ColumnContent("FS", "str", True, never_hidden=True)])
+auto_eval_column_dict.append(["is_5fewshot", ColumnContent, ColumnContent("IS_FS", "bool", True)])
+
+
+##### languages #############
+auto_eval_column_dict.append(["LANG", ColumnContent, ColumnContent("LANG", "str", True, never_hidden=True)])
+
+
+
+auto_eval_column_dict.append(["model", ColumnContent, ColumnContent("Model", "markdown", True, never_hidden=True)])
+#auto_eval_column_dict.append(["fewshot", ColumnContent, ColumnContent("Few-Shot", "str", True)])
+
+#Scores
+auto_eval_column_dict.append(["average", ColumnContent, ColumnContent("Avg. Combined Performance ⬆️", "number", True)])
+for task in Tasks:
+    auto_eval_column_dict.append([task.name, ColumnContent, ColumnContent(task.value.col_name, "number", True)])
+
+# Model information
+#auto_eval_column_dict.append(["model_type", ColumnContent, ColumnContent("Type", "str", False)])
+auto_eval_column_dict.append(["architecture", ColumnContent, ColumnContent("Architecture", "str", False)])
+auto_eval_column_dict.append(["weight_type", ColumnContent, ColumnContent("Weight type", "str", False, True)])
+#auto_eval_column_dict.append(["precision", ColumnContent, ColumnContent("Precision", "str", False)])
+auto_eval_column_dict.append(["license", ColumnContent, ColumnContent("Hub License", "str", False)])
+auto_eval_column_dict.append(["params", ColumnContent, ColumnContent("#Params (B)", "number", False)])
+auto_eval_column_dict.append(["likes", ColumnContent, ColumnContent("Hub ❤️", "number", False)])
+auto_eval_column_dict.append(["still_on_hub", ColumnContent, ColumnContent("Available on the hub", "bool", False)])
+auto_eval_column_dict.append(["revision", ColumnContent, ColumnContent("Model sha", "str", False, False)])
+
+# We use make dataclass to dynamically fill the scores from Tasks
+AutoEvalColumn = make_dataclass("AutoEvalColumn", auto_eval_column_dict, frozen=True)
+
+## For the queue columns in the submission tab
+@dataclass(frozen=True)
+class EvalQueueColumn:  # Queue column
+    model = ColumnContent("model", "markdown", True)
+    revision = ColumnContent("revision", "str", True)
+    private = ColumnContent("private", "bool", True)
+    #precision = ColumnContent("precision", "str", True)
+    weight_type = ColumnContent("weight_type", "str", "Original")
+    status = ColumnContent("status", "str", True)
+
+## All the model information that we might need
+@dataclass
+class ModelDetails:
+    name: str
+    display_name: str = ""
+    symbol: str = "" # emoji
+
+
+class ModelType(Enum):
+    PT = ModelDetails(name="pretrained", symbol="🟢")
+    FT = ModelDetails(name="fine-tuned", symbol="🔶")
+    IFT = ModelDetails(name="instruction-tuned", symbol="⭕")
+    RL = ModelDetails(name="RL-tuned", symbol="🟦")
+    Unknown = ModelDetails(name="", symbol="?")
+
+    def to_str(self, separator=" "):
+        return f"{self.value.symbol}{separator}{self.value.name}"
+
+    @staticmethod
+    def from_str(type):
+        if "fine-tuned" in type or "🔶" in type:
+            return ModelType.FT
+        if "pretrained" in type or "🟢" in type:
+            return ModelType.PT
+        if "RL-tuned" in type or "🟦" in type:
+            return ModelType.RL
+        if "instruction-tuned" in type or "⭕" in type:
+            return ModelType.IFT
+        return ModelType.Unknown
+
+@dataclass
+class FewShotDetails:
+    name: str
+    symbol: str = ""  # emoji
+
+class FewShotType(Enum):
+    ZS = FewShotDetails(name="zero-shot", symbol="0️⃣")
+    FS = FewShotDetails(name="5-few-shot", symbol="5️⃣")
+    Unknown = FewShotDetails(name="unknown", symbol="❓")
+
+    def to_str(self, separator=" "):
+        return f"{self.value.symbol}{separator}{self.value.name}"
+
+    @staticmethod
+    def from_num_fewshot(is_5fewshot):
+        """Determines FewShotType based on num_fewshot."""
+        if is_5fewshot is False:
+            return FewShotType.ZS
+        elif is_5fewshot is True:
+            return FewShotType.FS
+        return FewShotType.Unknown
+
+class WeightType(Enum):
+    Adapter = ModelDetails("Adapter")
+    Original = ModelDetails("Original")
+    Delta = ModelDetails("Delta")
+
+class Precision(Enum):
+    float16 = ModelDetails("float16")
+    bfloat16 = ModelDetails("bfloat16")
+    Unknown = ModelDetails("?")
+
+    def from_str(precision):
+        if precision in ["torch.float16", "float16"]:
+            return Precision.float16
+        if precision in ["torch.bfloat16", "bfloat16"]:
+            return Precision.bfloat16
+        return Precision.Unknown
+
+# Column selection
+COLS = [c.name for c in fields(AutoEvalColumn) if not c.hidden]
+
+EVAL_COLS = [c.name for c in fields(EvalQueueColumn)]
+EVAL_TYPES = [c.type for c in fields(EvalQueueColumn)]
+
+BENCHMARK_COLS = [t.value.col_name for t in Tasks]
+
+'''
+# Nuovi valori per CPS, AVERAGE, BEST, e ID nella tabella
+@dataclass
+class NewColumnContent:
+    name: str
+    type: str
+    displayed_by_default: bool
+    hidden: bool = False
+    never_hidden: bool = False
+'''
+
+'''
+new_column_dict = []
+# Aggiungi CPS, VERAGE, BEST, ID
+new_column_dict.append(["CPS", NewColumnContent, NewColumnContent("CPS", "number", True)])
+new_column_dict.append(["AVERAGE", NewColumnContent, NewColumnContent("Average ⬆️", "number", True)])
+new_column_dict.append(["BEST", NewColumnContent, NewColumnContent("Best Performance", "number", True)])
+new_column_dict.append(["ID", NewColumnContent, NewColumnContent("ID", "str", True)])
+NewColumn = make_dataclass("NewColumn", new_column_dict, frozen=True)
+NEW_COLS = [c.name for c in fields(NewColumn) if not c.hidden]
+'''

src/envs.py

@@ -0,0 +1,36 @@
+import os
+
+from huggingface_hub import HfApi
+
+# Info to change for your repository
+# ----------------------------------
+TOKEN = os.environ.get("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") # A read/write token for your org
+
+#OWNER = "giux78" # Change to your org - don't forget to create a results and request dataset, with the correct format!
+OWNER = "saeedfarzi"
+# ----------------------------------
+
+#REPO_ID = f"{OWNER}/leaderboard-evalita"
+#QUEUE_REPO = f"{OWNER}/evalita-requests"
+#RESULTS_REPO = f"{OWNER}/evalita-results"
+
+REPO_ID = f"{OWNER}/llm_leaderboard"
+QUEUE_REPO = f"{OWNER}/e3c_llm_requests"
+RESULTS_REPO = f"{OWNER}/e3c_llm_results"
+
+# If you setup a cache later, just change HF_HOME
+#CACHE_PATH=os.getenv("HF_HOME", "/home/sfarzi/leaderboard/")
+
+# Local caches
+#EVAL_REQUESTS_PATH = os.path.join(CACHE_PATH, "eval-queue")
+#EVAL_RESULTS_PATH = os.path.join(CACHE_PATH, "eval-results")
+#EVAL_REQUESTS_PATH_BACKEND = os.path.join(CACHE_PATH, "eval-queue-bk")
+#EVAL_RESULTS_PATH_BACKEND = os.path.join(CACHE_PATH, "eval-results-bk")
+
+EVAL_REQUESTS_PATH ='/home/sfarzi/leaderboard/llm_leaderboard/e3c_llm_requests' #os.path.join(CACHE_PATH, "eval-queue")
+EVAL_RESULTS_PATH = '/home/sfarzi/leaderboard/llm_leaderboard/e3c_llm_results'#os.path.join(CACHE_PATH, "eval-results")
+EVAL_REQUESTS_PATH_BACKEND = '/home/sfarzi/leaderboard/llm_leaderboard/e3c_llm_requests' #os.path.join(CACHE_PATH, "eval-queue-bk")
+EVAL_RESULTS_PATH_BACKEND = '/home/sfarzi/leaderboard/llm_leaderboard/e3c_llm_results' #os.path.join(CACHE_PATH, "eval-results-bk")
+
+
+API = HfApi(token=TOKEN)

src/leaderboard/.ipynb_checkpoints/read_evals-checkpoint.py

@@ -0,0 +1,214 @@
+import glob
+import json
+import math
+import os
+from dataclasses import dataclass
+
+import dateutil
+import numpy as np
+from typing import Dict, Union
+
+#from get_model_info import num_params
+from src.display.formatting import make_clickable_model
+from src.display.utils import AutoEvalColumn, ModelType, Tasks, Precision, WeightType, FewShotType
+from src.submission.check_validity import is_model_on_hub
+
+
+@dataclass
+class EvalResult:
+    """Represents one full evaluation. Built from a combination of the result and request file for a given run.
+    """
+    eval_name: str # org_model_precision (uid)
+    full_model: str # org/model (path on hub)
+    org: str 
+    model: str
+    revision: str # commit hash, "" if main
+    results: Dict[str, Union[float, int]]  # float o int
+    average_CPS: float
+    is_5fewshot: bool
+    fewshot_symbol: FewShotType = FewShotType.Unknown
+    weight_type: WeightType = WeightType.Original # Original or Adapter
+    architecture: str = "Unknown" 
+    license: str = "?"
+    likes: int = 0
+    num_params: int = 0
+    date: str = "" # submission date of request file
+    still_on_hub: bool = False
+
+    @classmethod
+    def init_from_json_file(self, json_filepath):
+        """Inits the result from the specific model result file"""
+        with open(json_filepath) as fp:
+            data = json.load(fp)
+
+        config = data.get("config")
+
+        #average_CPS = f"{data.get('average_CPS'):.2f}"
+        # Get average_CPS
+        average_CPS = float(data.get('average_CPS', 0.0))  # 0.0 come valore di default
+        # Get number of fewshot
+        fewshot = config.get("num_fewshot", False)
+
+        try:
+            if fewshot == "5":
+                is_5fewshot = True
+            else:
+                is_5fewshot = False
+        except ValueError:
+            is_5fewshot = False
+        # Determine the few-shot type (ZS or FS) based on num_fewshot
+        fewshot_symbol = FewShotType.from_num_fewshot(is_5fewshot)  # Use the new
+
+        # Determine the number of parameters of the models
+        num_params = int(0)
+        num_params_billion = config.get("num_params_billion")
+        if num_params_billion is not None:
+            num_params = math.ceil(num_params_billion)
+
+        # Get model and org
+        org_and_model = config.get("model_name", config.get("model_args", None))
+        org_and_model = org_and_model.split("/", 1)
+
+        if len(org_and_model) == 1:
+            org = None
+            model = org_and_model[0]
+            #result_key = f"{model}_{precision.value.name}"
+            result_key = f"{model}_{is_5fewshot}"
+        else:
+            org = org_and_model[0]
+            model = org_and_model[1]
+            #result_key = f"{org}_{model}_{precision.value.name}"
+            result_key = f"{org}_{model}_{is_5fewshot}"
+        full_model = "/".join(org_and_model)
+
+        still_on_hub, _, model_config = is_model_on_hub(
+            full_model, config.get("model_sha", "main"), trust_remote_code=True, test_tokenizer=False
+        )
+        architecture = "?"
+        if model_config is not None:
+            architectures = getattr(model_config, "architectures", None)
+            if architectures:
+                architecture = ";".join(architectures)
+
+        # Extract the results of the models
+        results = {}
+        for task in Tasks:
+            task = task.value
+
+            for k, v in data["tasks"].items():
+                if task.benchmark[:-2] == k:
+                    if "Best Prompt Id" in task.col_name:
+                        results[task.benchmark] = int(v[task.metric_type][-1:])
+                    else:
+                        #results[task.benchmark] = f"{v[task.metric_type]:.2f}"  # Ensure two decimals for display
+                        results[task.benchmark] = float(v[task.metric_type])
+                        #value = float(v[task.metric_type])
+                        #results[task.benchmark] = round(value, 2)  # Arrotonda a 2 decimali
+
+        return self(
+            eval_name=result_key,
+            full_model=full_model,
+            org=org,
+            model=model,
+            results=results,
+            average_CPS=average_CPS,
+            fewshot_symbol=fewshot_symbol,
+            is_5fewshot=is_5fewshot,
+            revision= config.get("model_sha", ""),
+            still_on_hub=still_on_hub,
+            architecture=architecture,
+            num_params=num_params
+        )
+
+    '''
+    def update_with_request_file(self, requests_path):
+        """Finds the relevant request file for the current model and updates info with it"""
+        request_file = get_request_file_for_model(requests_path, self.full_model, self.precision.value.name)
+
+        try:
+            with open(request_file, "r") as f:
+                request = json.load(f)
+            self.model_type = ModelType.from_str(request.get("model_type", ""))
+            self.weight_type = WeightType[request.get("weight_type", "Original")]
+            self.license = request.get("license", "?")
+            self.likes = request.get("likes", 0)
+            self.num_params = request.get("params", 0)
+            self.date = request.get("submitted_time", "")
+        except Exception:
+            print(f"Could not find request file for {self.org}/{self.model} with precision 
+    '''
+
+    def to_dict(self):
+        """Converts the Eval Result to a dict compatible with our dataframe display"""
+        average = self.average_CPS
+
+        fewshot_symbol = (
+            self.fewshot_symbol.value.symbol if isinstance(self.fewshot_symbol, FewShotType) else "❓"
+        )
+
+        data_dict = {
+            "eval_name": self.eval_name,  # not a column, just a save name,
+            #AutoEvalColumn.precision.name: self.precision.value.name,
+            #AutoEvalColumn.model_type.name: self.model_type.value.name,
+            #AutoEvalColumn.model_type_symbol.name: self.model_type.value.symbol,
+            #AutoEvalColumn.model_type.name: self.model_type.value.name if self.model_type else "Unknown",
+            #AutoEvalColumn.model_type_symbol.name: self.model_type.value.symbol if self.model_type else "Unknown",
+            AutoEvalColumn.fewshot_symbol.name: fewshot_symbol,
+            AutoEvalColumn.weight_type.name: self.weight_type.value.name,
+            AutoEvalColumn.architecture.name: self.architecture,
+            AutoEvalColumn.model.name: make_clickable_model(self.full_model),
+            AutoEvalColumn.revision.name: self.revision,
+            AutoEvalColumn.average.name: average,
+            AutoEvalColumn.is_5fewshot.name: self.is_5fewshot,
+            AutoEvalColumn.license.name: self.license,
+            AutoEvalColumn.likes.name: self.likes,
+            AutoEvalColumn.params.name: self.num_params,
+            AutoEvalColumn.still_on_hub.name: self.still_on_hub,
+        }
+
+        for task in Tasks:
+            data_dict[task.value.col_name] = self.results[task.value.benchmark]
+
+        return data_dict
+
+
+def get_raw_eval_results(results_path: str, requests_path: str) -> list[EvalResult]:
+    """From the path of the results folder root, extract all needed info for results"""
+    model_result_filepaths = []
+
+    for root, _, files in os.walk(results_path):
+        # We should only have json files in model results
+        if len(files) == 0 or any([not f.endswith(".json") for f in files]):
+            continue
+
+        # Sort the files by date
+        try:
+            files.sort(key=lambda x: x.removesuffix(".json").removeprefix("results_")[:-7])
+        except dateutil.parser._parser.ParserError:
+            files = [files[-1]]
+
+        for file in files:
+            model_result_filepaths.append(os.path.join(root, file))
+
+    eval_results = {}
+    for model_result_filepath in model_result_filepaths:
+        # Creation of result
+        eval_result = EvalResult.init_from_json_file(model_result_filepath)
+        #eval_result.update_with_request_file(requests_path)
+
+        # Store results of same eval together
+        eval_name = eval_result.eval_name
+        if eval_name in eval_results.keys():
+            eval_results[eval_name].results.update({k: v for k, v in eval_result.results.items() if v is not None})
+        else:
+            eval_results[eval_name] = eval_result
+
+    results = []
+    for v in eval_results.values():
+        try:
+            v.to_dict() # we test if the dict version is complete
+            results.append(v)
+        except KeyError:  # not all eval values present
+            continue
+
+    return results

src/leaderboard/read_evals.py

@@ -0,0 +1,257 @@
+import glob
+import json
+import math
+import os
+from dataclasses import dataclass
+
+import dateutil
+import numpy as np
+from typing import Dict, Union
+
+#from get_model_info import num_params
+from src.display.formatting import make_clickable_model
+from src.display.utils import AutoEvalColumn, ModelType, Tasks, Precision, WeightType, FewShotType
+from src.submission.check_validity import is_model_on_hub
+
+
+@dataclass
+class EvalResult:
+    """Represents one full evaluation. Built from a combination of the result and request file for a given run.
+    """
+    eval_name: str # org_model_precision (uid)
+    full_model: str # org/model (path on hub)
+    org: str 
+    model: str
+    revision: str # commit hash, "" if main
+    results: Dict[str, Union[float, int]]  # float o int
+    average_CPS: float
+    is_5fewshot: bool
+    Lang:str="EN"
+    fewshot_symbol: FewShotType = FewShotType.Unknown
+    weight_type: WeightType = WeightType.Original # Original or Adapter
+    architecture: str = "Unknown" 
+    license: str = "?"
+    likes: int = 0
+    num_params: int = 0
+    date: str = "" # submission date of request file
+    still_on_hub: bool = False
+
+    @classmethod
+    def init_from_json_file(self, json_filepath):
+        """Inits the result from the specific model result file"""
+        print ( "************ Reading file ****************")
+        print ("file name :" , json_filepath)
+        with open(json_filepath) as fp:
+            data = json.load(fp)
+        #print(json_filepath,data)
+        config = data.get("config")
+        print (config)
+        #print( data)
+        #average_CPS = f"{data.get('average_CPS'):.2f}"
+        # Get average_CPS
+        average_CPS = float(data.get('average_CPS', 0.0))  # 0.0 come valore di default
+        # Get number of fewshot
+        fewshot = config.get("num_fewshot", False)
+        Lang=config.get("LANG", "EN")
+        try:
+            if fewshot == "5":
+                is_5fewshot = True
+            else:
+                is_5fewshot = False
+        except ValueError:
+            is_5fewshot = False
+        # Determine the few-shot type (ZS or FS) based on num_fewshot
+        fewshot_symbol = FewShotType.from_num_fewshot(is_5fewshot)  # Use the new
+
+        # Determine the number of parameters of the models
+        num_params = int(0)
+        num_params_billion = config.get("num_params_billion")
+        if num_params_billion is not None:
+            num_params = math.ceil(num_params_billion)
+
+        # Get model and org
+        org_and_model = config.get("model_name", config.get("model_args", None))
+        org_and_model = org_and_model.split("/", 1)
+
+        if len(org_and_model) == 1:
+            org = None
+            model = org_and_model[0]
+            #result_key = f"{model}_{precision.value.name}"
+            result_key = f"{model}_{is_5fewshot}"
+        else:
+            org = org_and_model[0]
+            model = org_and_model[1]
+            #result_key = f"{org}_{model}_{precision.value.name}"
+            result_key = f"{org}_{model}_{is_5fewshot}"
+        full_model = "/".join(org_and_model)
+
+        still_on_hub, _, model_config = is_model_on_hub(
+            full_model, config.get("model_sha", "main"), trust_remote_code=True, test_tokenizer=False
+        )
+        architecture = "?"
+        if model_config is not None:
+            architectures = getattr(model_config, "architectures", None)
+            if architectures:
+                architecture = ";".join(architectures)
+
+        # Extract the results of the models
+        results = {}
+        for task in Tasks:
+            #print(task, "data:", data["tasks"].items())
+            task = task.value
+
+            for k, v in data["tasks"].items():
+                
+                if task.benchmark[:-2] == k:
+                    if "Best Prompt Id" in task.col_name:
+                        #print ("k:", k,"v:", v)
+                        #print (task.metric_type)
+                        #print(v[task.metric_type]) 
+                        results[task.benchmark] = int(v[task.metric_type][-1:])
+                    else:
+                        #results[task.benchmark] = f"{v[task.metric_type]:.2f}"  # Ensure two decimals for display
+                        results[task.benchmark] = float(v[task.metric_type])
+                        #value = float(v[task.metric_type])
+                        #results[task.benchmark] = round(value, 2)  # Arrotonda a 2 decimali
+        
+        print ("Generated Object: ",self(
+            eval_name=result_key+"_"+Lang,
+            full_model=full_model,
+            Lang=Lang,
+            org=org,
+            model=model,
+            results=results,
+            average_CPS=average_CPS,
+            fewshot_symbol=fewshot_symbol,
+            is_5fewshot=is_5fewshot,
+            revision= config.get("model_sha", ""),
+            still_on_hub=still_on_hub,
+            architecture=architecture,
+            num_params=num_params
+        ))
+        print ( "************ End of Reading file ****************")
+        return self(
+            eval_name=result_key+"_"+Lang,
+            full_model=full_model,
+            Lang=Lang,
+            org=org,
+            model=model,
+            results=results,
+            average_CPS=average_CPS,
+            fewshot_symbol=fewshot_symbol,
+            is_5fewshot=is_5fewshot,
+            revision= config.get("model_sha", ""),
+            still_on_hub=still_on_hub,
+            architecture=architecture,
+            num_params=num_params
+        )
+
+    '''
+    def update_with_request_file(self, requests_path):
+        """Finds the relevant request file for the current model and updates info with it"""
+        request_file = get_request_file_for_model(requests_path, self.full_model, self.precision.value.name)
+
+        try:
+            with open(request_file, "r") as f:
+                request = json.load(f)
+            self.model_type = ModelType.from_str(request.get("model_type", ""))
+            self.weight_type = WeightType[request.get("weight_type", "Original")]
+            self.license = request.get("license", "?")
+            self.likes = request.get("likes", 0)
+            self.num_params = request.get("params", 0)
+            self.date = request.get("submitted_time", "")
+        except Exception:
+            print(f"Could not find request file for {self.org}/{self.model} with precision 
+    '''
+
+    def to_dict(self):
+        """Converts the Eval Result to a dict compatible with our dataframe display"""
+        average = self.average_CPS
+        
+        
+        fewshot_symbol = (
+            self.fewshot_symbol.value.symbol if isinstance(self.fewshot_symbol, FewShotType) else "❓"
+        )
+        
+        data_dict = {
+            "eval_name": self.eval_name,  # not a column, just a save name,
+            #AutoEvalColumn.precision.name: self.precision.value.name,
+            #AutoEvalColumn.model_type.name: self.model_type.value.name,
+            #AutoEvalColumn.model_type_symbol.name: self.model_type.value.symbol,
+            #AutoEvalColumn.model_type.name: self.model_type.value.name if self.model_type else "Unknown",
+            #AutoEvalColumn.model_type_symbol.name: self.model_type.value.symbol if self.model_type else "Unknown",
+            AutoEvalColumn.fewshot_symbol.name: fewshot_symbol,
+            AutoEvalColumn.weight_type.name: self.weight_type.value.name,
+            AutoEvalColumn.architecture.name: self.architecture,
+            AutoEvalColumn.model.name: make_clickable_model(self.full_model),
+            AutoEvalColumn.revision.name: self.revision,
+            AutoEvalColumn.average.name: average,
+            AutoEvalColumn.is_5fewshot.name: self.is_5fewshot,
+            AutoEvalColumn.license.name: self.license,
+            AutoEvalColumn.likes.name: self.likes,
+            AutoEvalColumn.params.name: self.num_params,
+            AutoEvalColumn.still_on_hub.name: self.still_on_hub,
+            AutoEvalColumn.LANG.name:self.Lang
+        }
+
+        for task in Tasks:
+            data_dict[task.value.col_name] = self.results[task.value.benchmark]
+
+        return data_dict
+
+
+def get_raw_eval_results(results_path: str, requests_path: str) -> list[EvalResult]:
+    """From the path of the results folder root, extract all needed info for results"""
+    model_result_filepaths = []
+
+    for root, _, files in os.walk(results_path):
+        # We should only have json files in model results
+        #print(root,files)
+        if len(files) == 0 or any([not f.endswith(".json") for f in files]):
+            continue
+        #if len(files) == 0 : continue
+        #json_files = [f for f in files if f.endswith(".json")]
+        #if not json_files:
+             #continue
+        # Sort the files by date
+        #print(root,files)
+        try:
+            files.sort(key=lambda x: x.removesuffix(".json").removeprefix("results_")[:-7])
+        except dateutil.parser._parser.ParserError:
+            files = [files[-1]]
+        
+        for file in files:
+            if not file.endswith(".json"):continue
+            model_result_filepaths.append(os.path.join(root, file))
+
+    #print(model_result_filepaths)
+    eval_results = {}
+    for model_result_filepath in model_result_filepaths:
+        # Creation of result
+        eval_result = EvalResult.init_from_json_file(model_result_filepath)
+        #eval_result.update_with_request_file(requests_path)
+        #print ("************************")
+        #print("path: ", model_result_filepath)
+        #print('eval_result: ',eval_result)
+        # Store results of same eval together
+        eval_name = eval_result.eval_name
+        print('eval_name: ',eval_name)
+        print ("lang: ", eval_result.Lang)
+        
+        if ( eval_name in eval_results.keys()) :
+            eval_results[eval_name].results.update({k: v for k, v in eval_result.results.items() if v is not None})
+        else:
+            eval_results[eval_name] = eval_result
+
+    results = []
+    #print("eval_results:   ",eval_results)
+    for v in eval_results.values():
+        try:
+            v.to_dict() # we test if the dict version is complete
+            results.append(v)
+        except KeyError:  # not all eval values present
+            print ("Except(error) : line 244 file read_evals.py")
+            continue
+    print("Final results:   ",results)
+    print ("@@@@@@@@@@@@")
+    return results

src/populate.py

@@ -0,0 +1,62 @@
+import json
+import os
+
+import pandas as pd
+
+from src.display.formatting import has_no_nan_values, make_clickable_model
+from src.display.utils import AutoEvalColumn, EvalQueueColumn
+from src.leaderboard.read_evals import get_raw_eval_results
+
+
+def get_leaderboard_df(results_path: str, requests_path: str, cols: list, benchmark_cols: list) -> pd.DataFrame:
+    """Creates a dataframe from all the individual experiment results"""
+    print (results_path, requests_path)
+    raw_data = get_raw_eval_results(results_path, requests_path)
+    print(raw_data)
+    all_data_json = [v.to_dict() for v in raw_data]
+
+    df = pd.DataFrame.from_records(all_data_json)
+    
+    print ("all_data_json: ", all_data_json)
+    df = df.sort_values(by=[AutoEvalColumn.average.name], ascending=False)
+    df = df[cols].round(decimals=2)
+
+    # filter out if any of the benchmarks have not been produced
+    df = df[has_no_nan_values(df, benchmark_cols)]
+    return df
+
+
+def get_evaluation_queue_df(save_path: str, cols: list) -> list[pd.DataFrame]:
+    """Creates the different dataframes for the evaluation queues requestes"""
+    entries = [entry for entry in os.listdir(save_path) if not entry.startswith(".")]
+    all_evals = []
+
+    for entry in entries:
+        if ".json" in entry:
+            file_path = os.path.join(save_path, entry)
+            with open(file_path) as fp:
+                data = json.load(fp)
+
+            data[EvalQueueColumn.model.name] = make_clickable_model(data["model"])
+            data[EvalQueueColumn.revision.name] = data.get("revision", "main")
+
+            all_evals.append(data)
+        elif ".md" not in entry:
+            # this is a folder
+            sub_entries = [e for e in os.listdir(f"{save_path}/{entry}") if os.path.isfile(e) and not e.startswith(".")]
+            for sub_entry in sub_entries:
+                file_path = os.path.join(save_path, entry, sub_entry)
+                with open(file_path) as fp:
+                    data = json.load(fp)
+
+                data[EvalQueueColumn.model.name] = make_clickable_model(data["model"])
+                data[EvalQueueColumn.revision.name] = data.get("revision", "main")
+                all_evals.append(data)
+
+    pending_list = [e for e in all_evals if e["status"] in ["PENDING", "RERUN"]]
+    running_list = [e for e in all_evals if e["status"] == "RUNNING"]
+    finished_list = [e for e in all_evals if e["status"].startswith("FINISHED") or e["status"] == "PENDING_NEW_EVAL"]
+    df_pending = pd.DataFrame.from_records(pending_list, columns=cols)
+    df_running = pd.DataFrame.from_records(running_list, columns=cols)
+    df_finished = pd.DataFrame.from_records(finished_list, columns=cols)
+    return df_finished[cols], df_running[cols], df_pending[cols]

src/submission/check_validity.py

@@ -0,0 +1,99 @@
+import json
+import os
+import re
+from collections import defaultdict
+from datetime import datetime, timedelta, timezone
+
+import huggingface_hub
+from huggingface_hub import ModelCard
+from huggingface_hub.hf_api import ModelInfo
+from transformers import AutoConfig
+from transformers.models.auto.tokenization_auto import AutoTokenizer
+
+def check_model_card(repo_id: str) -> tuple[bool, str]:
+    """Checks if the model card and license exist and have been filled"""
+    try:
+        card = ModelCard.load(repo_id)
+    except huggingface_hub.utils.EntryNotFoundError:
+        return False, "Please add a model card to your model to explain how you trained/fine-tuned it."
+
+    # Enforce license metadata
+    if card.data.license is None:
+        if not ("license_name" in card.data and "license_link" in card.data):
+            return False, (
+                "License not found. Please add a license to your model card using the `license` metadata or a"
+                " `license_name`/`license_link` pair."
+            )
+
+    # Enforce card content
+    if len(card.text) < 200:
+        return False, "Please add a description to your model card, it is too short."
+
+    return True, ""
+
+def is_model_on_hub(model_name: str, revision: str, token: str = None, trust_remote_code=False, test_tokenizer=False) -> tuple[bool, str]:
+    """Checks if the model model_name is on the hub, and whether it (and its tokenizer) can be loaded with AutoClasses."""
+    try:
+        config = AutoConfig.from_pretrained(model_name, revision=revision, trust_remote_code=trust_remote_code, token=token)
+        if test_tokenizer:
+            try:
+                tk = AutoTokenizer.from_pretrained(model_name, revision=revision, trust_remote_code=trust_remote_code, token=token)
+            except ValueError as e:
+                return (
+                    False,
+                    f"uses a tokenizer which is not in a transformers release: {e}",
+                    None
+                )
+            except Exception as e:
+                return (False, "'s tokenizer cannot be loaded. Is your tokenizer class in a stable transformers release, and correctly configured?", None)
+        return True, None, config
+
+    except ValueError:
+        return (
+            False,
+            "needs to be launched with `trust_remote_code=True`. For safety reason, we do not allow these models to be automatically submitted to the leaderboard.",
+            None
+        )
+
+    except Exception as e:
+        return False, "was not found on hub!", None
+
+
+def get_model_size(model_info: ModelInfo, precision: str):
+    """Gets the model size from the configuration, or the model name if the configuration does not contain the information."""
+    try:
+        model_size = round(model_info.safetensors["total"] / 1e9, 3)
+    except (AttributeError, TypeError):
+        return 0  # Unknown model sizes are indicated as 0, see NUMERIC_INTERVALS in example_app.py
+
+    size_factor = 8 if (precision == "GPTQ" or "gptq" in model_info.modelId.lower()) else 1
+    model_size = size_factor * model_size
+    return model_size
+
+def get_model_arch(model_info: ModelInfo):
+    """Gets the model architecture from the configuration"""
+    return model_info.config.get("architectures", "Unknown")
+
+def already_submitted_models(requested_models_dir: str) -> set[str]:
+    """Gather a list of already submitted models to avoid duplicates"""
+    depth = 1
+    file_names = []
+    users_to_submission_dates = defaultdict(list)
+
+    for root, _, files in os.walk(requested_models_dir):
+        current_depth = root.count(os.sep) - requested_models_dir.count(os.sep)
+        if current_depth == depth:
+            for file in files:
+                if not file.endswith(".json"):
+                    continue
+                with open(os.path.join(root, file), "r") as f:
+                    info = json.load(f)
+                    file_names.append(f"{info['model']}_{info['revision']}_{info['precision']}")
+
+                    # Select organisation
+                    if info["model"].count("/") == 0 or "submitted_time" not in info:
+                        continue
+                    organisation, _ = info["model"].split("/")
+                    users_to_submission_dates[organisation].append(info["submitted_time"])
+
+    return set(file_names), users_to_submission_dates

src/submission/submit.py

@@ -0,0 +1,119 @@
+import json
+import os
+from datetime import datetime, timezone
+
+from src.display.formatting import styled_error, styled_message, styled_warning
+from src.envs import API, EVAL_REQUESTS_PATH, TOKEN, QUEUE_REPO
+from src.submission.check_validity import (
+    already_submitted_models,
+    check_model_card,
+    get_model_size,
+    is_model_on_hub,
+)
+
+REQUESTED_MODELS = None
+USERS_TO_SUBMISSION_DATES = None
+
+def add_new_eval(
+    model: str,
+    base_model: str,
+    revision: str,
+    precision: str,
+    weight_type: str,
+    model_type: str,
+):
+    global REQUESTED_MODELS
+    global USERS_TO_SUBMISSION_DATES
+    if not REQUESTED_MODELS:
+        REQUESTED_MODELS, USERS_TO_SUBMISSION_DATES = already_submitted_models(EVAL_REQUESTS_PATH)
+
+    user_name = ""
+    model_path = model
+    if "/" in model:
+        user_name = model.split("/")[0]
+        model_path = model.split("/")[1]
+
+    precision = precision.split(" ")[0]
+    current_time = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
+
+    if model_type is None or model_type == "":
+        return styled_error("Please select a model type.")
+
+    # Does the model actually exist?
+    if revision == "":
+        revision = "main"
+
+    # Is the model on the hub?
+    if weight_type in ["Delta", "Adapter"]:
+        base_model_on_hub, error, _ = is_model_on_hub(model_name=base_model, revision=revision, token=TOKEN, test_tokenizer=True)
+        if not base_model_on_hub:
+            return styled_error(f'Base model "{base_model}" {error}')
+
+    if not weight_type == "Adapter":
+        model_on_hub, error, _ = is_model_on_hub(model_name=model, revision=revision, token=TOKEN, test_tokenizer=True)
+        if not model_on_hub:
+            return styled_error(f'Model "{model}" {error}')
+
+    # Is the model info correctly filled?
+    try:
+        model_info = API.model_info(repo_id=model, revision=revision)
+    except Exception:
+        return styled_error("Could not get your model information. Please fill it up properly.")
+
+    model_size = get_model_size(model_info=model_info, precision=precision)
+
+    # Were the model card and license filled?
+    try:
+        license = model_info.cardData["license"]
+    except Exception:
+        return styled_error("Please select a license for your model")
+
+    modelcard_OK, error_msg = check_model_card(model)
+    if not modelcard_OK:
+        return styled_error(error_msg)
+
+    # Seems good, creating the eval
+    print("Adding new eval")
+
+    eval_entry = {
+        "model": model,
+        "base_model": base_model,
+        "revision": revision,
+        "precision": precision,
+        "weight_type": weight_type,
+        "status": "PENDING",
+        "submitted_time": current_time,
+        "model_type": model_type,
+        "likes": model_info.likes,
+        "params": model_size,
+        "license": license,
+        "private": False,
+    }
+
+    # Check for duplicate submission
+    if f"{model}_{revision}_{precision}" in REQUESTED_MODELS:
+        return styled_warning("This model has been already submitted.")
+
+    print("Creating eval file")
+    OUT_DIR = f"{EVAL_REQUESTS_PATH}/{user_name}"
+    os.makedirs(OUT_DIR, exist_ok=True)
+    out_path = f"{OUT_DIR}/{model_path}_eval_request_False_{precision}_{weight_type}.json"
+
+    with open(out_path, "w") as f:
+        f.write(json.dumps(eval_entry))
+
+    print("Uploading eval file")
+    API.upload_file(
+        path_or_fileobj=out_path,
+        path_in_repo=out_path.split("eval-queue/")[1],
+        repo_id=QUEUE_REPO,
+        repo_type="dataset",
+        commit_message=f"Add {model} to eval queue",
+    )
+
+    # Remove the local file
+    os.remove(out_path)
+
+    return styled_message(
+        "Your request has been submitted to the evaluation queue!\nPlease wait for up to an hour for the model to show in the PENDING list."
+    )

src/tasks.py

@@ -0,0 +1,183 @@
+from dataclasses import dataclass
+from enum import Enum
+
+@dataclass
+class Task:
+    benchmark: str
+    # metric: str
+    accuracy: str
+    col_name: str
+
+NUM_FEWSHOT = 0  # Change with your few shot
+# ---------------------------------------------------
+
+# Your leaderboard name
+TITLE = """<h1 align="center" id="space-title">🚀 EVALITA-LLM Leaderboard 🚀</h1>"""
+
+# What does your leaderboard evaluate?
+INTRODUCTION_TEXT = """
+Evalita-LLM is a benchmark designed to evaluate Large Language Models (LLMs) on Italian tasks. The distinguishing features of Evalita-LLM are the following: (i) all tasks are native Italian, avoiding translation issues and potential cultural biases; (ii) the benchmark includes generative tasks, enabling more natural interaction with LLMs; (iii) all tasks are evaluated against multiple prompts, this way mitigating the model sensitivity to specific prompts and allowing a fairer evaluation.
+"""
+
+#MEASURE_DESCRIPTION = "Combined Performance = (1 - (Best_Prompt - Prompt_Average) / 100) * Best_Prompt. Prompt Average = accuracy averaged over the six prompts. Best Prompt = accuracy of the best prompt. Prompt ID = ID of the best prompt (see legend above)"
+MEASURE_DESCRIPTION = "<small>**Combined Performance** = (1 - (**Best Prompt** - **Prompt Average**) / 100) * **Best Prompt**. **Prompt Average** = accuracy averaged over the assessed prompts. **Best Prompt** = accuracy of the best prompt. **Prompt ID** = ID of the best prompt (see legend above).</small>"
+#MEASURE_DESCRIPTION = "<small>**Combined Performance** = (1 - (**Best Prompt** - **Prompt Average**) / 100) * **Best Prompt**. **Prompt Average** = avg. accuracy over prompts. **Best Prompt** = accuracy of best prompt. **Prompt ID** = ID of the best prompt (see legend above).</small>"
+
+# Tasks Descriptions
+TE_DESCRIPTION = """### Textual Entailment (TE) --- *Multiple-choice task*
+    The input are two sentences: the text (T) and the hypothesis (H). The model  has to determine whether the meaning of the hypothesis is logically entailed by the text.
+
+| #   | Prompt | Answer Choices |
+|-----|------------|--------------|
+| 1   | La frase: '{{text1}}' implica logicamente che la frase: '{{text2}}' sia vera? | ["Sì", "No"] |
+| 2   | Devi risolvere un compito di inferenza semantica. La frase: '{{text1}}' implica logicamente che la frase: '{{text2}}' sia vera? | ["Sì", "No"] |
+| 3   | La frase: '{{text1}}' implica logicamente che la frase: '{{text2}}' sia vera?\\nA: Sì\\nB: No\\nRisposta: | ["A", "B"] |
+| 4   | Devi risolvere un compito di inferenza semantica. La frase: '{{text1}}' implica logicamente che la frase: '{{text2}}' sia vera?\\nA: Sì\\nB: No\\nRisposta: | ["A", "B"] |
+| 5   | Frase 1: '{{text1}}' Frase 2: '{{text2}}' | ["La frase 1 implica logicamente che la frase 2 sia vera", "La frase 1 non implica logicamente che la frase 2 sia vera"] |
+| 6   | Devi risolvere un compito di inferenza semantica. Frase 1: '{{text1}}' Frase 2: '{{text2}}' | ["La frase 1 implica logicamente che la frase 2 sia vera", "La frase 1 non implica logicamente che la frase 2 sia vera"] |
+
+<small>**Combined Performance** = (1 - (**Best Prompt** - **Prompt Average**) / 100) * **Best Prompt**. **Prompt Average** = accuracy averaged over the 6 prompts. **Best Prompt** = accuracy of the best prompt. **Prompt ID** = ID of the best prompt (see legend above). </small>
+
+"""
+
+SA_DESCRIPTION = """### Sentiment Analysis (SA) --- *Multiple-choice task*
+    The input is a tweet. The model has to determine the sentiment polarity of the text, categorizing it into one of four classes: positive, negative, neutral, or mixed.
+
+| #   | Prompt                                                                                       | Answer Choices               |
+|-----|--------------------------------------------------------------------------------|-----------------------------|
+| 1   | Qual è il sentiment espresso nel seguente tweet: '{{text}}'?                     | ["Positivo", "Negativo", "Neutro", "Misto"] |
+| 2   | Devi svolgere un compito di analisi del sentiment. Qual è il sentiment espresso nel seguente tweet: '{{text}}'? | ["Positivo", "Negativo", "Neutro", "Misto"] |
+| 3   | Qual è il sentiment espresso nel seguente tweet: '{{text}}'?\\nA: Positivo\\nB: Negativo\\nC: Neutro\\nD: Misto\\nRisposta: | ["A", "B", "C", "D"] |
+| 4   | Devi svolgere un compito di analisi del sentiment. Qual è il sentiment espresso nel seguente tweet: '{{text}}'?\\nA: Positivo\\nB: Negativo\\nC: Neutro\\nD: \\nRisposta: | ["A", "B", "C", "D"] |
+| 5   | Il seguente tweet: '{{text}}' esprime un sentiment                                | ["Positivo", "Negativo", "Neutro", "Misto"]  |
+| 6   | Devi svolgere un compito di analisi del sentiment. Il seguente tweet: '{{text}}' esprime un sentiment | ["Positivo", "Negativo", "Neutro", "Misto"] |
+
+<small>**Combined Performance** = (1 - (**Best Prompt** - **Prompt Average**) / 100) * **Best Prompt**. **Prompt Average** = F1-macro averaged over the 6 prompts. **Best Prompt** = F1-macro of the best prompt. **Prompt ID** = ID of the best prompt (see legend above). </small>
+
+"""
+
+HS_DESCRIPTION = """### Hate Speech (HS) --- *Multiple-choice task*
+    The input is a tweet. The model has to determine whether the text contains hateful content directed towards marginalized or minority groups. The output is a binary classification: hateful or not hateful.
+
+| #   | Prompt                                                                                       | Answer Choices                                   |
+|-----|--------------------------------------------------------------------------------|-------------------------------------------------|
+| 1   | C'è incitamento all'odio nel seguente tweet: '{{full_text}}'?                     | ["Falso", "Vero"] |
+| 2   | Devi svolgere un compito di identificazione di incitamento all'odio. C'è incitamento all'odio nel seguente tweet: '{{full_text}}'? | ["Falso", "Vero"] |
+| 3   | C'è incitamento all'odio nel seguente tweet: '{{full_text}}'?\\nA: \\nB: Falso\\nRisposta: | ["B", "A"]                                      |
+| 4   | Devi svolgere un compito di identificazione di incitamento all'odio. C'è incitamento all'odio nel seguente tweet: '{{full_text}}'?\\nA: \\nB: \\nRisposta: | ["B", "A"]                                      |
+| 5   | Il tweet: '{{full_text}}'                                                      | ["non contiene incitamento all'odio", "contiene incitamento all'odio"] |
+| 6   | Devi svolgere un compito di identificazione di incitamento all'odio. Il tweet: '{{full_text}}' | ["non contiene incitamento all'odio", "contiene incitamento all'odio"] |
+
+<small>**Combined Performance** = (1 - (**Best Prompt** - **Prompt Average**) / 100) * **Best Prompt**. **Prompt Average** = F1-micro averaged over the 6 prompts. **Best Prompt** = F1-micro of the best prompt. **Prompt ID** = ID of the best prompt (see legend above). </small>
+
+"""
+
+AT_DESCRIPTION = """### Admission Tests (AT) --- *Multiple-choice task*
+    The input is a multiple-choice question with five options (A-E) from Italian medical specialty entrance exams, and the model must identify the correct answer.
+
+| #   | Prompt                                                                                       | Answer Choices               |
+|-----|--------------------------------------------------------------------------------|-----------------------------|
+| 1   | Dato il seguente quesito di medicina: '{{Question}}' qual è la risposta corretta? | ["A", "B", "C", "D", "E"]   |
+| 2   | Devi risolvere un compito di risposte a domande. Dato il seguente quesito di medicina: '{{Question}}' qual è la risposta corretta? | ["A", "B", "C", "D", "E"]   |
+| 3   | Dato il seguente quesito di medicina: '{{Question}}' qual è la risposta corretta?\\nA: {{A}}\\nB: {{B}}\\nC: {{C}}\\nD: {{D}}\\nE: {{E}}\\nRisposta: | ["A", "B", "C", "D", "E"]   |
+| 4   | Devi risolvere un compito a scelta multipla. Dato il seguente caso clinico: '{{background}}', qual è la risposta corretta alla domanda: '{{domanda}}'?\\nA: {{A}}\\nB: {{B}}\\nC: {{C}}\\nD: {{D}}\\nE: {{E}}\\nRisposta:Devi risolvere un compito a scelta multipla. Dato il seguente quesito di medicina: '{{Question}}' qual è la risposta corretta?\\nA: {{A}}\\nB: {{B}}\\nC: {{C}}\\nD: {{D}}\\nE: {{E}}\\nRisposta: | ["A", "B", "C", "D", "E"]   |
+| 5   | Dato il seguente caso clinico: '{{background}}'. La risposta corretta alla domanda: '{{domanda}}' èDato il seguente quesito di medicina '{{Question}}' la risposta corretta è: | ["A", "B", "C", "D", "E"]   |
+| 6   | Devi risolvere un compito di risposte a domande. Dato il seguente quesito di medicina '{{Question}}' la risposta corretta è: | ["A", "B", "C", "D", "E"]   |
+
+<small>**Combined Performance** = (1 - (**Best Prompt** - **Prompt Average**) / 100) * **Best Prompt**. **Prompt Average** = accuracy averaged over the 6 prompts. **Best Prompt** = accuracy of the best prompt. **Prompt ID** = ID of the best prompt (see legend above). </small>
+
+"""
+
+WIC_DESCRIPTION = """### Word in Context (WIC) --- *Multiple-choice task*
+    The input consists of a word (w) and two sentences. The model has to determine whether the word w has the same meaning in both sentences. The output is a binary classification: 1 (same meaning) or 0 (different meaning).
+
+| #   | Prompt                                                                                       | Answer Choices                                   |
+|-----|--------------------------------------------------------------------------------|-------------------------------------------------|
+| 1   | La parola: '{{sentence1[start1:end1]}}' nella frase: '{{sentence1}}' ha lo stesso significato della parola: '{{sentence2[start2:end2]}}' nella frase: '{{sentence2}}'? | ["No", "Sì"]                         |
+| 2   | Devi determinare se una stessa parola usata in due frasi differenti ha lo stesso significato in entrambi i contesti. La parola: '{{sentence1[start1:end1]}}' nella frase: '{{sentence1}}' ha lo stesso significato della parola: '{{sentence2[start2:end2]}}' nella frase: '{{sentence2}}'? | ["No", "Sì"] |
+| 3   | La parola: '{{sentence1[start1:end1]}}' nella frase: '{{sentence1}}' ha lo stesso significato della parola: '{{sentence2[start2:end2]}}' nella frase: '{{sentence2}}'?\\nA: Sì\\nB: No\\nRisposta: | ["B", "A"]                                      |
+| 4   | Devi determinare se una stessa parola usata in due frasi differenti ha lo stesso significato in entrambi i contesti. La parola: '{{sentence1[start1:end1]}}' nella frase: '{{sentence1}}' ha lo stesso significato della parola: '{{sentence2[start2:end2]}}' nella frase: '{{sentence2}}'?\\nA: \\nB: No\\nRisposta: | ["B", "A"]  |
+| 5   | La parola: '{{sentence1[start1:end1]}}' nella frase: '{{sentence1}}' e la parola: '{{sentence2[start2:end2]}}' nella frase: '{{sentence2}}' | ["non hanno lo stesso significato", "hanno lo stesso significato"] |
+| 6   | Devi determinare se una stessa parola usata in due frasi differenti ha lo stesso significato in entrambi i contesti. La parola: '{{sentence1[start1:end1]}}' nella frase: '{{sentence1}}' e la parola: '{{sentence2[start2:end2]}}' nella frase: '{{sentence2}}' | ["non hanno lo stesso significato", "hanno lo stesso significato"] |
+
+<small>**Combined Performance** = (1 - (**Best Prompt** - **Prompt Average**) / 100) * **Best Prompt**. **Prompt Average** = F1-macro averaged over the 6 prompts. **Best Prompt** = F1-macro of the best prompt. **Prompt ID** = ID of the best prompt (see legend above). </small>
+
+"""
+
+FAQ_DESCRIPTION = """### Frequently Asked Questions & Question Answering (FAQ) --- *Multiple-choice task*
+    The input is a user query regarding the water supply service. The model must identify the correct answer from the 4 available options.
+
+| #   | Prompt                                                                                       | Answer Choices               |
+|-----|--------------------------------------------------------------------------------|-----------------------------|
+| 1   | Rispondi alla seguente domanda: '{{question}}'                                              | {{[A, B, C, D]}}            |
+| 2   | Devi risolvere un compito di risposte a domande. Rispondi alla seguente domanda: '{{question}}' | {{[A, B, C, D]}}            |
+| 3   | Rispondi alla seguente domanda: '{{question}}'\\nA: {{A}}\\nB: {{B}}\\nC: {{C}}\\nD: {{D}}\\nRisposta: | ["A", "B", "C", "D"] |
+| 4   | Devi risolvere un compito a scelta multipla. Rispondi alla seguente domanda: '{{question}}'\\nA: {{A}}\\nB: {{B}}\\nC: {{C}}\\nD: {{D}}\\nRisposta: | ["A", "B", "C", "D"] |
+| 5   | La risposta alla domanda: '{{question}}' è:                                                | {{[A, B, C, D]}}            |
+| 6   | Devi risolvere un compito di risposte a domande. La risposta alla domanda: '{{question}}' è: | {{[A, B, C, D]}}  |
+
+<small>**Combined Performance** = (1 - (**Best Prompt** - **Prompt Average**) / 100) * **Best Prompt**. **Prompt Average** = accuracy averaged over the 6 prompts. **Best Prompt** = accuracy of the best prompt. **Prompt ID** = ID of the best prompt (see legend above). </small>
+
+"""
+
+LS_DESCRIPTION = """### Lexical Substitution (LS) --- *Generative task*
+    The input is a sentence containing a target word (w). The model has to replace the target word w with its most suitable synonyms that are contextually relevant. 
+
+| #   | Prompt                                                                                       |
+|-----|--------------------------------------------------------------------------------|
+| 1   | Trova 10 parole che possono sostituire la parola racchiusa tra i marcatori `<head>` nella seguente frase: '{{context}}', mantenendo lo stesso significato. Elenca i lemmi (forme base) di queste parole, separandoli con una virgola, ad esempio: lemma1, lemma2, lemma3, lemma4, lemma5. Non aggiungere commenti o altro testo. Risposta: |
+| 2   | Devi risolvere un compito di sostituzione lessicale. Trova 10 parole che possono sostituire la parola racchiusa tra i marcatori `<head>` nella seguente frase: '{{context}}', mantenendo lo stesso significato. Elenca i lemmi (forme base) di queste parole, separandoli con una virgola, ad esempio: lemma1, lemma2, lemma3, lemma4, lemma5. Non aggiungere commenti o altro testo. Risposta: |
+
+<small>**Combined Performance** = (1 - (**Best Prompt** - **Prompt Average**) / 100) * **Best Prompt**. **Prompt Average** = F1 averaged over the 2 prompts. **Best Prompt** = F1 of the best prompt. **Prompt ID** = ID of the best prompt (see legend above). </small>
+
+"""
+
+SU_DESCRIPTION = """### Summarization (SUM) --- *Generative task*
+    The input is a news article. The model has to generate a concise summary of the input text, capturing the key information and main points.
+
+| #   | Prompt                                                                                       |
+|-----|--------------------------------------------------------------------------------|
+| 1   | Riassumi il seguente articolo di giornale: '{{source}}'\\nRiassunto:             |
+| 2   | Devi risolvere un compito di sintesi automatica del testo. Riassumi il seguente articolo di giornale: '{{source}}'\\nRiassunto: |
+
+<small>**Combined Performance** = (1 - (**Best Prompt** - **Prompt Average**) / 100) * **Best Prompt**. **Prompt Average** = F1 averaged over the 2 prompts. **Best Prompt** = F1 of the best prompt. **Prompt ID** = ID of the best prompt (see legend above). </small>
+
+"""
+
+NER_DESCRIPTION = """### Named Entity Recognition (NER) --- *Generative task*
+    The input is a sentence. The model has to identify and classify Named Entities into predefined categories such as person, organization, and location.
+
+| #   | Prompt                                                                                       |
+|-----|--------------------------------------------------------------------------------|
+| 1   | Estrai tutte le entità di tipo PER (persona), LOC (luogo) e ORG (organizzazione) dal testo seguente. Riporta ogni entità con il formato: Entità$Tipo, separando ciascuna coppia con ','. Se non ci sono entità da estrarre, rispondi con '&&NOENT&&'.\\nTesto: '{{text}}'\\nEntità: |
+| 2   | Devi svolgere un compito di riconoscimento delle entità nei testi. Estrai tutte le entità di tipo PER (persona), LOC (luogo) e ORG (organizzazione) dal testo seguente. Riporta ogni entità con il formato: Entità$Tipo, separando ciascuna coppia con ','. Se non ci sono entità da estrarre, rispondi con '&&NOENT&&'.\\nTesto: '{{text}}'\\nEntità: |
+
+<small>**Combined Performance** = (1 - (**Best Prompt** - **Prompt Average**) / 100) * **Best Prompt**. **Prompt Average** = F1 averaged over the 2 prompts. **Best Prompt** = F1 of the best prompt. **Prompt ID** = ID of the best prompt (see legend above). </small>
+
+"""
+
+REL_DESCRIPTION = """### Relation Extraction (REL) --- *Generative task*
+    The input is a sentence of a clinical text. The model must identify and extract relationships between laboratory test results (e.g., blood pressure) and the corresponding tests or procedures that generated them (e.g., blood pressure test).
+
+| #   | Prompt                                                                                       |
+|-----|--------------------------------------------------------------------------------|
+| 1   | Dato un documento medico devi estrarre tutte le misurazioni degli esami medici presenti. Riporta ogni relazione nel formato: misurazione$esame, separando ciascuna coppia con '%'. Se non ci sono relazioni da estrarre, rispondi con '&&NOREL&&'.\\nTesto: '{{text}}'\\nRelazioni: |
+| 2   | Devi svolgere un compito di estrazione di relazioni da documenti medici. Dato un documento medico devi estrarre tutte le misurazioni degli esami medici presenti. Riporta ogni relazione nel formato: misurazione$esame, separando ciascuna coppia con '%'. Se non ci sono relazioni da estrarre, rispondi con '&&NOREL&&'.\\nTesto: '{{text}}'\\nRelazioni: |
+
+<small>**Combined Performance** = (1 - (**Best Prompt** - **Prompt Average**) / 100) * **Best Prompt**. **Prompt Average** = F1 averaged over the 2 prompts. **Best Prompt** = F1 of the best prompt. **Prompt ID** = ID of the best prompt (see legend above). </small>
+
+"""
+
+# Create a dictionary to map task names to their descriptions
+TASK_DESCRIPTIONS = {
+    "TE": TE_DESCRIPTION,
+    "SA": SA_DESCRIPTION,
+    "HS": HS_DESCRIPTION,
+    "AT": AT_DESCRIPTION,
+    "WIC": WIC_DESCRIPTION,
+    "FAQ": FAQ_DESCRIPTION,
+    "LS": LS_DESCRIPTION,
+    "SU": SU_DESCRIPTION,
+    "NER": NER_DESCRIPTION,
+    "REL": REL_DESCRIPTION
+}