for k, v in metrics.items()
}
Finally, we can print out the results. We can see that overall the scores are higher when the output is semantically correct, and also when the output closely matches with the gold-standard answer.
for i, eg in enumerate(examples):
    score_string = ", ".join([f"{k}={v['examples'][i]['value']:.4f}" for k, v in eval_results.items()])
    print(f"Example {i}:")
    print("Question: " + predictions[i]['query'])
    print("Real Answer: " + predictions[i]['answer'])
    print("Predicted Answer: " + predictions[i]['result'])
    print("Predicted Scores: " + score_string)
    print()
Example 0:
Question: What did the president say about Ketanji Brown Jackson
Real Answer: He praised her legal ability and said he nominated her for the supreme court.
Predicted Answer:  The president said that she is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He also said that she is a consensus builder and that she has received a broad range of support from the Fraternal Order of Police to former judges appointed by both Democrats and Republicans.
Predicted Scores: rouge=0.0941, chrf=0.2001, bert_score=0.5219, uni_eval=0.9043
Example 1:
Question: What did the president say about Michael Jackson
Real Answer: Nothing
Predicted Answer:  The president did not mention Michael Jackson in this speech.
Predicted Scores: rouge=0.0000, chrf=0.1087, bert_score=0.3486, uni_eval=0.7802
rtdocs_stable/api.python.langchain.com/en/stable/use_cases/evaluation/data_augmented_question_answering.html