--- title: Hierarchical Softmax Loss datasets: - danieldux/ISCO-08 tags: - evaluate - metric description: "TODO: add a description here" sdk: gradio sdk_version: 3.19.1 app_file: app.py pinned: false --- # Metric Card for Hierarchical Softmax Loss ***Module Card Instructions:*** *Fill out the following subsections. Feel free to take a look at existing metric cards if you'd like examples.* ## Metric Description *Give a brief overview of this metric, including what task(s) it is usually used for, if any.* ## How to Use *Give general statement of how to use the metric* *Provide simplest possible example for using the metric* ### Inputs *List all input arguments in the format below* - **input_field** *(type): Definition of input, with explanation if necessary. State any default value(s).* ### 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.*