question, despite lacking depth or creativity.\n\n[[B]]\n',     'value': 'B',     'score': 0}Customize the Evaluation Promptâ€‹You can use your own custom evaluation prompt to add more task-specific instructions or to instruct the evaluator to score the output.*Note: If you use a prompt that expects generates a result in a unique format, you may also have to pass in a custom output parser (output_parser=your_parser()) instead of the default PairwiseStringResultOutputParserfrom langchain.prompts import PromptTemplateprompt_template = PromptTemplate.from_template(    """Given the input context, which is most similar to the reference label: A or B?Reason step by step and finally, respond with either [[A]] or [[B]] on its own line.DATA----input: {input}reference: {reference}A: {prediction}B: {prediction_b}---Reasoning:""")evaluator = load_evaluator(    "pairwise_string", prompt=prompt_template, requires_reference=True)# The prompt was assigned to the evaluatorprint(evaluator.prompt)    input_variables=['input', 'prediction', 'prediction_b', 'reference'] output_parser=None partial_variables={} template='Given the input context, which is most similar to the reference label: A or B?\nReason step by step and finally, respond with either [[A]] or [[B]] on its own line.\n\nDATA\n----\ninput: {input}\nreference: {reference}\nA: {prediction}\nB: {prediction_b}\n---\nReasoning:\n\n' template_format='f-string' validate_template=Trueevaluator.evaluate_string_pairs(    prediction="The dog that ate the ice cream was named fido.",    prediction_b="The dog's name is spot",    input="What is the name