= eval_chain.evaluate(    predicted_dataset, predictions, question_key="input", prediction_key="output")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': 28, ' INCORRECT': 5})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]    {'input': 'What are the four common sense steps that the author suggests to move forward safely?',     'answer': 'The four common sense steps suggested by the author to move forward safely are: stay protected with vaccines and treatments, prepare for new variants, end the shutdown of schools and businesses, and stay vigilant.',     'output': 'The four common sense steps suggested in the most recent State of the Union address are: cutting the cost of prescription drugs, providing a pathway to citizenship for Dreamers, revising laws so businesses have the workers they need and families donâ€™t wait decades to reunite, and protecting access to health care and preserving a womanâ€™s right to choose.',     'grade': ' INCORRECT'}PreviousExamplesNextComparing Chain OutputsLoading the dataSetting up a chainMake a predictionMake many predictionsEvaluate performanceCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright Â© 2023 LangChain, Inc.