[[10]]", 'score': 10}Output Format‚ÄãAs shown above, the scoring evaluators return a dictionary with the following values:score: A score between 1 and 10 with 10 being the best.reasoning: String "chain of thought reasoning" from the LLM generated prior to creating the scorePreviousRegex MatchNextString DistanceUsage with Ground TruthUsage without referencesCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
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. ->: [[10]]", 'score': 10}Output Format‚ÄãAs shown above, the scoring evaluators return a dictionary with the following values:score: A score between 1 and 10 with 10 being the best.reasoning: String "chain of thought reasoning" from the LLM generated prior to creating the scorePreviousRegex MatchNextString DistanceUsage with Ground TruthUsage without referencesCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.