Typo (huggingface#1430)

Author: pcuenca

codellama.md

@@ -327,7 +327,7 @@ You can learn more on how to [Deploy LLMs with Hugging Face Inference Endpoints
 
 ## Evaluation
 
-Language models for code are typically benchmarked on datatsets such as HumanEval. It consists of programming challenges where the model is presented with a function signature and a docstring and is tasked to complete the function body. The proposed solution is then verified by running a set of predefined unit tests. Finally, a pass rate is reported which describes how many solutions passed all tests. The pass@1 rate describes how often the model generates a passing solution when having one shot whereas pass@10 describes how often at least one solution passes out of 10 proposed candidates.
+Language models for code are typically benchmarked on datasets such as HumanEval. It consists of programming challenges where the model is presented with a function signature and a docstring and is tasked to complete the function body. The proposed solution is then verified by running a set of predefined unit tests. Finally, a pass rate is reported which describes how many solutions passed all tests. The pass@1 rate describes how often the model generates a passing solution when having one shot whereas pass@10 describes how often at least one solution passes out of 10 proposed candidates.
 
 While HumanEval is a Python benchmark there have been significant efforts to translate it to more programming languages and thus enable a more holistic evaluation. One such approach is [MultiPL-E](https://github.com/nuprl/MultiPL-E) which translates HumanEval to over a dozen languages. We are hosting a [multilingual code leaderboard](https://huggingface.co/spaces/bigcode/multilingual-code-evals) based on it to allow the community to compare models across different languages to evaluate which model fits their use-case best.