Update metric description in README.md

README.md

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 ***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
-The ISCO-08 Hierarchical Accuracy Measure is an implementation of the measure described in [Functional Annotation of Genes Using Hierarchical Text Categorization](https://www.researchgate.net/publication/44046343_Functional_Annotation_of_Genes_Using_Hierarchical_Text_Categorization) (Kiritchenko, Svetlana and Famili, Fazel. 2005) and adapted for the ISCO-08 classification scheme by the International Labour Organization.
 
 The measure accounts for the hierarchical structure of the ISCO-08 classification scheme by rewarding partial correctness in classification, penalizing distant errors, and applying higher penalties for higher-level errors. It is designed to evaluate the accuracy of predictions within a hierarchical occupation classification system, such as the ISCO-08 scheme, which organizes occupations into a tree structure with four levels: major group, sub-major group, minor group, and unit group.
 

metric_template_1.py

@@ -35,36 +35,7 @@ _CITATION = """
 """
 
 _DESCRIPTION = """
-The ISCO-08 Hierarchical Accuracy Measure is an implementation 
-of the measure described in [Functional Annotation of Genes Using Hierarchical Text Categorization](https://www.researchgate.net/publication/44046343_Functional_Annotation_of_Genes_Using_Hierarchical_Text_Categorization) 
-(Kiritchenko, Svetlana and Famili, Fazel. 2005) with the ISCO-08 taxonomy by the International Labour Organization.
-
-1. The measure gives credit to partially correct classification, 
-e.g. misclassification into node $I$ (ISCO unit group "1120") 
-when the correct category is $G$ (ISCO unit group "1111") 
-should be penalized less than misclassification into node $D$ 
-(e.g., ISCO unit group "1211") since $I$ is in the same subgraph (ISCO sub-major group "11")
-as $G$ and $D$ is not.
-2. The measure punishes distant errors more heavily:
-    1. the measure gives higher evaluation for correctly classifying one level down compared to staying at the parent node, e.g. classification into node $E$ (ISCO minor group "111") is better than classification into its parent $C$ (ISCO sub-major group "11") since $E$ is closer to the correct category $G$; 
-    2. the measure gives lower evaluation for incorrectly classifying one level down comparing to staying at the parent node, e.g. classification into node $F$ (ISCO minor group "112") is worse than classification into its parent $C$ since $F$ is farther away from $G$.
-
-The features described are accomplished by pairing hierarchical variants of precision ($hP$) and recall ($hR$) to form a hierarchical F1 ($hF_β$) score where each sample belongs not only to its class (e.g., a unit group level code), but also to all ancestors of the class in a hierarchical graph (i.e., the minor, sub-major, and major group level codes).
-
-Hierarchical precision can be computed with:
-`$hP = \frac{| \v{C}_i ∩ \v{C}^′_i|}  {|\v{C}^′_i |} = \frac{1}{2}$`
-
-Hierarchical recall can be computed with:
-`$hR = \frac{| \v{C}_i ∩ \v{C}^′_i|}  {|\v{C}_i |} = \frac{1}{2}$`
-
-Combining the two values $hP$ and $hR$ into one hF-measure:
-`$hF_β = \frac{(β^2 + 1) · hP · hR}{(β^2 · hP + hR)}, β ∈ [0, +∞)$`
-
-Note:
-**TP**: True positive
-**TN**: True negative
-**FP**: False positive
-**FN**: False negative
+The ISCO-08 Hierarchical Accuracy Measure is an implementation of the measure described in [Functional Annotation of Genes Using Hierarchical Text Categorization](https://www.researchgate.net/publication/44046343_Functional_Annotation_of_Genes_Using_Hierarchical_Text_Categorization) (Kiritchenko, Svetlana and Famili, Fazel. 2005) and adapted for the ISCO-08 classification scheme by the International Labour Organization.
 """
 
 _KWARGS_DESCRIPTION = """