= eval_chain.evaluate(    predicted_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': 3, ' 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': 'How many employees are also customers?',     'answer': 'None',     'result': ' 59 employees are also customers.',     'grade': ' INCORRECT'}PreviousQuestion AnsweringNextDebuggingLoading the dataSetting up a chainMake a predictionMake many predictionsEvaluate performanceCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright Â© 2023 LangChain, Inc.