retrieval_leaderboard.py

from pathlib import Path
from utils import load_json_results
from leaderboard_tab import search_leaderboard, update_columns_to_show, create_leaderboard_tab

# Constants
RETRIEVAL_ABOUT_SECTION = """
## About Retrieval Evaluation

The retrieval evaluation assesses a model's ability to find and retrieve relevant information from a large corpus of Arabic text. Models are evaluated on:

### Web Search Dataset Metrics
- **MRR (Mean Reciprocal Rank)**: Measures the ranking quality by focusing on the position of the first relevant result
- **nDCG (Normalized Discounted Cumulative Gain)**: Evaluates the ranking quality considering all relevant results
- **Recall@5**: Measures the proportion of relevant documents found in the top 5 results
- **Overall Score**: Combined score calculated as the average of MRR, nDCG, and Recall@5

### Model Requirements
- Must support Arabic text embeddings
- Should handle queries of at least 512 tokens 
- Must work with `sentence-transformers` library

### Evaluation Process
1. Models process Arabic web search queries
2. Retrieved documents are evaluated using:
   - MRR for first relevant result positioning
   - nDCG for overall ranking quality
   - Recall@5 for top results accuracy
3. Metrics are averaged to calculate the overall score
4. Models are ranked based on their overall performance

### How to Prepare Your Model
- Ensure your model is publicly available on HuggingFace Hub (We don't support private model evaluations yet)
- Model should output fixed-dimension embeddings for text
- Support batch processing for efficient evaluation (this is default if you use `sentence-transformers`)
"""

# Global variables
retrieval_df = None

def load_retrieval_results(prepare_for_display=False, sort_col=None, drop_cols=None):
    dataframe_path = Path(__file__).parent / "results" / "retrieval_results.json"
    return load_json_results(
        dataframe_path,
        prepare_for_display=prepare_for_display,
        sort_col=sort_col,
        drop_cols=drop_cols
    )

def load_retrieval_leaderboard():
    """Load and prepare the retrieval leaderboard data"""
    global retrieval_df
    
    # Prepare retrieval dataframe
    retrieval_df = load_retrieval_results(True, "Average Score", drop_cols=["Revision", "Precision", "Task"])
    retrieval_df.insert(0, "Rank", range(1, 1 + len(retrieval_df)))
    
    return retrieval_df

def retrieval_search_leaderboard(model_name, columns_to_show):
    """Search function for retrieval leaderboard"""
    return search_leaderboard(retrieval_df, model_name, columns_to_show)

def update_retrieval_columns_to_show(columns_to_show):
    """Update displayed columns for retrieval leaderboard"""
    return update_columns_to_show(retrieval_df, columns_to_show)

def create_retrieval_tab():
    """Create the complete retrieval leaderboard tab"""
    global retrieval_df
    
    # Load data if not already loaded
    if retrieval_df is None:
        retrieval_df = load_retrieval_leaderboard()
    
    # Define default columns to show
    default_columns = ["Rank", "Model", "Average Score", "Model Size (MB)", "Context Length",
                       "Embedding Dimension", "Web Search Dataset", "Islamic Knowledge Dataset"]
    
    # Create and return the tab
    return create_leaderboard_tab(
        df=retrieval_df,
        initial_columns_to_show=default_columns,
        search_function=retrieval_search_leaderboard,
        update_function=update_retrieval_columns_to_show,
        about_section=RETRIEVAL_ABOUT_SECTION,
        task_type="Retriever"
    )