# function to create new evaluator given data split
def create_recall_evaluator(set_name, k=1):
    """
        Create triplet evaluator for "train", "valid", or "test" split
    """

    return ImageTextRetrievalEvaluator(
        images=dataset[f"{set_name}"]["anchor"],
        texts=dataset[f"{set_name}"]["positive"],
        name=f"yt-title-thumbnail-{set_name}",
        k=k
    )

# Create new evaluator with Recall@k
evaluator_recall_train = create_recall_evaluator("train", k=1)
evaluator_recall_valid = create_recall_evaluator("valid", k=1)

print("Train:", evaluator_recall_train(model))
print("Valid:", evaluator_recall_valid(model))

# >> Train: {'yt-title-thumbnail-train_Recall@1': 0.660377358490566}
# >> Valid: {'yt-title-thumbnail-valid_Recall@1': 0.6363636363636364}