MonoT5 .predict gives NaN

[Max Tian]

Trying to use MonoT5 3B on some custom reranking tasks, the gist of the code is

model_name = "castorini/monot5-3b-msmarco-10k"
cross_encoder_model = MonoT5(model_name, token_false='▁false', token_true='▁true')

query, doc = "some query", "some doc"
cross_encoder_model.predict([(query, doc)], batch_size=1)

and returns [nan]

is this an expected case? should we treat this as a score of 0 in this case?

[Hossein A. Rahmani]

Hi, have you found the solution for this?

[Soyoung Yoon]

Hi, It's not exactly the same issue, but I've noticed similar issues on the reranking task.
For example, I'm using the following code.

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)

It may be too late, but the order inside the results is preserved, so the following code can be used as a quick workaround:

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

Using this code, it output correct scores for ndcg, recall.. and so on.