Update README.md

README.md

@@ -53,7 +53,7 @@ It is impratical for us to manually set specific configuration for each fine-tun
 Thus, OpenCSG strained our brains to provide a relatively fair method to compare the fine-tuned models on HumanEval benchmark.
 To simplify the comparison, we chosed the Pass@1 metric on python language, but our finetuning dataset includes samples in multi language.
 
-**For fair, we evaluated the fine-tuned and origin codellama models only with the original cases' prompts, not including any other instruction else.**
+**For fair, we evaluated the fine-tuned and original codellama models only with the original cases' prompts, not including any other instruction else.**
 
 **Otherwise, we use greedy decoding method for each model during the evaluation.**