Custom Evaluators for LLMs using Langchain with example and codes

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This video demonstrates how to build custom Evaluators and metrics to analyze performance of LLMs for Supervised and Unsupervised problems with codes using Langchain #machinelearning #artificialintelligence #datascience #langchain #python #llm

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Hi everyone, I'm getting the error` ValueError: Invalid output: . Output must contain a double bracketed string with the verdict between 1 and 10.` when testing a labeled_score_string evaluator with an open-source llm-model.Could you confirm the evaluator works with models appart from OpenAI. Thanks!!!
`evaluator = load_evaluator("labeled_score_string", criteria=criteria, llm=llm, normalize_by=10)
eval_result = can find them in the dresser's third drawer.", reference="The socks are in the third drawer in the dresser", input="Where are my socks?")`

laurafernandezbecerra

Custom Evaluators for LLMs using Langchain with example and codes

Custom Evaluators for LLMs using Langchain with example and codes

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