{'question': 'What is the purpose of the NATO Alliance?',     'answer': 'The purpose of the NATO Alliance is to secure peace and stability in Europe after World War 2.',     'result': ' The purpose of the NATO Alliance is to secure peace and stability in Europe after World War 2.'}Next, we can use a language model to score them programaticallyfrom langchain.evaluation.qa import QAEvalChainllm = OpenAI(temperature=0)eval_chain = QAEvalChain.from_llm(llm)graded_outputs = eval_chain.evaluate(    dataset, predictions, question_key="question", prediction_key="result")We can add in the graded output to the predictions dict and then get a count of the grades.for i, prediction in enumerate(predictions):    prediction["grade"] = graded_outputs[i]["text"]from collections import CounterCounter([pred["grade"] for pred in predictions])    Counter({' CORRECT': 7, ' INCORRECT': 4})We can also filter the datapoints to the incorrect examples and look at them.incorrect = [pred for pred in predictions if pred["grade"] == " INCORRECT"]incorrect[0]    {'question': 'What is the U.S. Department of Justice doing to combat the crimes of Russian oligarchs?',     'answer': 'The U.S. Department of Justice is assembling a dedicated task force to go after the crimes of Russian oligarchs.',     'result': ' The U.S. Department of Justice is assembling a dedicated task force to go after the crimes of Russian oligarchs and is naming a chief prosecutor for pandemic fraud.',     'grade': ' INCORRECT'}PreviousQuestion Answering Benchmarking: Paul Graham EssayNextQA GenerationLoading the dataSetting up a chainMake a predictionMake many predictionsEvaluate