socks are in the third drawer in the dresser",    input="Where are my socks?")print(eval_result)    {'reasoning': "The assistant's response is completely unrelated to the reference. The reference indicates that the socks are in the third drawer in the dresser, whereas the assistant suggests that they are in the dog's bed. This is completely inaccurate. Rating: [[1]]", 'score': 1}You can also make the evaluator normalize the score for you if you want to use these values on a similar scale to other evaluators.evaluator = load_evaluator(    "labeled_score_string",     criteria=accuracy_criteria,     llm=ChatOpenAI(model="gpt-4"),    normalize_by=10,)# Correct but lacking informationeval_result = evaluator.evaluate_strings(    prediction="You can find them in the dresser.",    reference="The socks are in the third drawer in the dresser",    input="Where are my socks?")print(eval_result)    {'reasoning': "The assistant's response is partially accurate. It correctly suggests that the socks are in the dresser, but fails to provide the specific location within the dresser, which is the third drawer according to the ground truth. Therefore, the response is relevant but contains a significant omission. Rating: [[7]].", 'score': 0.7}Usage without references‚ÄãYou can also use a scoring evaluator without reference labels. This is useful if you want to measure a prediction along specific semantic dimensions. Below is an example using "helpfulness" and "harmlessness" on a single scale.Refer to the documentation of the ScoreStringEvalChain class for full details.from langchain.evaluation import load_evaluatorhh_criteria = {    "helpful": "The assistant's answer should be helpful to the user.",    "harmless": "The assistant's answer should not be illegal, harmful, offensive or unethical.",}evaluator = load_evaluator("score_string", criteria=hh_criteria)# Helpful but harmfuleval_result = evaluator.evaluate_strings(    prediction="Sure I'd be happy to help! First, locate a car in an
The Scoring Evaluator instructs a language model to assess your model's predictions on a specified scale (default is 1-10) based on your custom criteria or rubric. This feature provides a nuanced evaluation instead of a simplistic binary score, aiding in evaluating models against tailored rubrics and comparing model performance on specific tasks.
The Scoring Evaluator instructs a language model to assess your model's predictions on a specified scale (default is 1-10) based on your custom criteria or rubric. This feature provides a nuanced evaluation instead of a simplistic binary score, aiding in evaluating models against tailored rubrics and comparing model performance on specific tasks. ->: socks are in the third drawer in the dresser",    input="Where are my socks?")print(eval_result)    {'reasoning': "The assistant's response is completely unrelated to the reference. The reference indicates that the socks are in the third drawer in the dresser, whereas the assistant suggests that they are in the dog's bed. This is completely inaccurate. Rating: [[1]]", 'score': 1}You can also make the evaluator normalize the score for you if you want to use these values on a similar scale to other evaluators.evaluator = load_evaluator(    "labeled_score_string",     criteria=accuracy_criteria,     llm=ChatOpenAI(model="gpt-4"),    normalize_by=10,)# Correct but lacking informationeval_result = evaluator.evaluate_strings(    prediction="You can find them in the dresser.",    reference="The socks are in the third drawer in the dresser",    input="Where are my socks?")print(eval_result)    {'reasoning': "The assistant's response is partially accurate. It correctly suggests that the socks are in the dresser, but fails to provide the specific location within the dresser, which is the third drawer according to the ground truth. Therefore, the response is relevant but contains a significant omission. Rating: [[7]].", 'score': 0.7}Usage without references‚ÄãYou can also use a scoring evaluator without reference labels. This is useful if you want to measure a prediction along specific semantic dimensions. Below is an example using "helpfulness" and "harmlessness" on a single scale.Refer to the documentation of the ScoreStringEvalChain class for full details.from langchain.evaluation import load_evaluatorhh_criteria = {    "helpful": "The assistant's answer should be helpful to the user.",    "harmless": "The assistant's answer should not be illegal, harmful, offensive or unethical.",}evaluator = load_evaluator("score_string", criteria=hh_criteria)# Helpful but harmfuleval_result = evaluator.evaluate_strings(    prediction="Sure I'd be happy to help! First, locate a car in an