--- title: Mean Reciprocal Rank colorFrom: blue colorTo: red sdk: gradio sdk_version: 3.0.2 app_file: app.py pinned: false tags: - evaluate - metric description: >- Mean Reciprocal Rank is a statistic measure for evaluating any process that produces a list of possible responses to a sample of queries, ordered by probability of correctness. --- # Metric Card for Mean Reciprocal Rank a statistic measure for evaluating any process that produces a list of possible responses to a sample of queries, ordered by probability of correctness. ## Metric Description The reciprocal rank of a query response is the multiplicative inverse of the rank of the first correct answer: 1 for first place, 1⁄2 for second place, 1⁄3 for third place and so on. The mean reciprocal rank is the average of the reciprocal ranks of results for a sample of queries Q {\text{MRR}}={\frac {1}{|Q|}}\sum _{{i=1}}^{{|Q|}}{\frac {1}{{\text{rank}}_{i}}}.\! ## How to Use Provide a list of gold ranks, where each item is rank of gold item of which the first rank starts with zero. ### Inputs *List all input arguments in the format below* - **input_field** *(List[int]): a list of integer where each integer is the rank of gold item ### Output Values *Explain what this metric outputs and provide an example of what the metric output looks like. Modules should return a dictionary with one or multiple key-value pairs, e.g. {"bleu" : 6.02}* *State the range of possible values that the metric's output can take, as well as what in that range is considered good. For example: "This metric can take on any value between 0 and 100, inclusive. Higher scores are better."* #### Values from Popular Papers *Give examples, preferrably with links to leaderboards or publications, to papers that have reported this metric, along with the values they have reported.* ### Examples *Give code examples of the metric being used. Try to include examples that clear up any potential ambiguity left from the metric description above. If possible, provide a range of examples that show both typical and atypical results, as well as examples where a variety of input parameters are passed.* ## Limitations and Bias *Note any known limitations or biases that the metric has, with links and references if possible.* ## Citation *Cite the source where this metric was introduced.* ## Further References *Add any useful further references.*