MonoT5 .predict gives NaN about beir HOT 2 OPEN

def do_evaluation(queries, qrels, corpus, results=None):
    k_values = [1,5,10,20,50,100]
    retriever = EvaluateRetrieval()
    from beir.reranking.models import CrossEncoder, MonoT5
    from beir.reranking import Rerank
    cross_encoder_model = MonoT5(mode, token_false='▁false', token_true='▁true')
    print(f"Loading cross-encoder model from: {cross_encoder_model.model.config._name_or_path}")
    reranker = Rerank(cross_encoder_model, batch_size=256)
    results = reranker.rerank(corpus, queries, results, top_k=100) # outputs nan scores to results
    results = remove_nan(results) # manually assign score due to bug
    ndcg, _map, recall, precision = retriever.evaluate(qrels, results, k_values)

def remove_nan(results):
    new_res = {}
    for query_key in results.keys():
        out = {}
        for i, corpus_key in enumerate(results[query_key].keys()):
            out[corpus_key] = 100 - i
        new_res[query_key] = out
    return new_res