def get_raw_scores(examples, preds):
    """
    Computes the exact and f1 scores from the examples and the model predictions
    """
    exact_scores = {}
    f1_scores = {}

    for example in examples:
        qas_id = example.qas_id
        gold_answers = [answer["text"] for answer in example.answers if normalize_answer(answer["text"])]

        if not gold_answers:
            # For unanswerable questions, only correct answer is empty string
            gold_answers = [""]

        if qas_id not in preds:
            print("Missing prediction for %s" % qas_id)
            continue

        prediction = preds[qas_id]
        exact_scores[qas_id] = max(compute_exact(a, prediction) for a in gold_answers)
        f1_scores[qas_id] = max(compute_f1(a, prediction) for a in gold_answers)

    return exact_scores, f1_scores

    Computes the exact and f1 scores from the examples and the model predictions
    
structured_prompting/fairseq-version/struprompting/data/squad/squad_metrics.py