readme updated

README.md

@@ -58,26 +58,28 @@ It uses the same definition as in previous case, but it works with multiset of l
     }
 
 ### Inputs
-*List all input arguments in the format below*
-- **input_field** *(type): Definition of input, with explanation if necessary. State any default value(s).*
+- **predictions** *(list[Union[int,str]]): list of predictions to score. Each predictions should be a list of predicted labels*
+- **references** *(list[Union[int,str]]): list of reference for each prediction. Each reference should be a list of reference labels*
 
 ### 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.*
+This metric outputs a dictionary, containing:
+- precision 
+- recall
+- accuracy
+- fscore
 
 ## Citation
-*Cite the source where this metric was introduced.*
 
-## Further References
-*Add any useful further references.*
+```bibtex
+@article{Zhang2014ARO,
+  title={A Review on Multi-Label Learning Algorithms},
+  author={Min-Ling Zhang and Zhi-Hua Zhou},
+  journal={IEEE Transactions on Knowledge and Data Engineering},
+  year={2014},
+  volume={26},
+  pages={1819-1837},
+  url={https://api.semanticscholar.org/CorpusID:1008003}
+}
+```
+