def METHOD_NAME(model, data_loader):
    """
    Predicts the data labels.
    Args:
        model (obj:`SemanticIndexBase`): A model to extract text embedding or calculate similarity of text pair.
        data_loader (obj:`List(Example)`): The processed data ids of text pair: [query_input_ids, query_token_type_ids, title_input_ids, title_token_type_ids]
    Returns:
        results(obj:`List`): cosine similarity of text pairs.
    """
    cosine_sims = []
    model.eval()
    with paddle.no_grad():
        for batch_data in data_loader:
            query_input_ids, query_token_type_ids, title_input_ids, title_token_type_ids = batch_data
            batch_cosine_sim = model.cosine_sim(
                query_input_ids=query_input_ids,
                title_input_ids=title_input_ids,
                query_token_type_ids=query_token_type_ids,
                title_token_type_ids=title_token_type_ids,
            ).numpy()
            cosine_sims.append(batch_cosine_sim)
        cosine_sims = np.concatenate(cosine_sims, axis=0)
        return cosine_sims