eval_example/embedding_eval.py

import os
import argparse

from beir.retrieval.evaluation import EvaluateRetrieval
from beir.retrieval.search.dense import DenseRetrievalExactSearch as DRES

from utils import load_data

import torch

from visual_embedding_model import DSERetriever

def get_args():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        '--dataset',
        type=str,
        help='Dataset Name which will be parsed to datasets.load_dataset function',
        default='nvidia/miracl-vision'
    )
    parser.add_argument(
        '--language',
        type=str,
        help='language to evaluate',
        default='sw'
    )
    return parser.parse_args()      

if __name__ == '__main__':
    args = get_args()
    tracker = None

    queries, corpus, qrels, images = load_data(
        args.dataset, 
        args.language
    ) 
    model = DSERetriever(
        model_name_or_path='MrLight/dse-qwen2-2b-mrl-v1',
        images=images
    )
    dres_model = DRES(
        model, 
        corpus_chunk_size=250000,
        batch_size=8
    )    
    retriever = EvaluateRetrieval(
        dres_model, 
        score_function='dot', 
        k_values = [1,5,10,100]
    )

    results = retriever.retrieve(corpus, queries)

    ndcg, map_, recall, precision = retriever.evaluate(qrels, results, retriever.k_values, ignore_identical_ids=True)
    print(ndcg, map_, recall, precision)