From bc84df5e3823a30c74515b678d50547e2e561163 Mon Sep 17 00:00:00 2001 From: wassname Date: Sat, 29 Apr 2023 18:46:58 +0800 Subject: [PATCH] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 80d0236..c751234 100644 --- a/README.md +++ b/README.md @@ -91,7 +91,7 @@ A great way to find new instruction datasets is to There are multiple ways to formally evaluate LLM capabilities. Right now project generally use one of these 3 libraries. Personally I prefer Eleuther's work, but opinions and github stars are divided. - python api: - - [huggingface/evaluate](https://github.com/huggingface/evaluate) this is not specific to LLM's or RLHF, but [some](https://github.com/nomic-ai/gpt4all/blob/main/eval_self_instruct.py#L43) [projects](https://github.com/gururise/AlpacaDataCleaned/blob/791174f63e/eval/README.md) find it and easy to use starting point. + - [huggingface/evaluate](https://github.com/huggingface/evaluate) this is not specific to LLM's or RLHF, but some [projects](https://github.com/gururise/AlpacaDataCleaned/blob/791174f63e/eval/README.md) find it and easy to use starting point. - cli api: - [EleutherAI/lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) - has lots of datasets like GLUE and ETHICS already included, works with huggingface - [openai/evals: Evals is a framework for evaluating LLMs and LLM systems, and an open-source registry of benchmarks.](https://github.com/openai/evals) - has lots of rare eval sets like sarcasm, works with langchain