Update README.md

README.md

@@ -1,19 +1,19 @@
----
-license: cc-by-4.0
-task_categories:
-- text-classification
-language:
-- en
-tags:
-- NLP
-- LLM
-- hierarchical
-- multi-label
-- classification
-pretty_name: WOS Hierarchical Multi-Label Text Classification
-size_categories:
-- 10K<n<100K
----
+---
+license: cc-by-4.0
+task_categories:
+- text-classification
+language:
+- en
+tags:
+- NLP
+- LLM
+- hierarchical
+- multi-label
+- classification
+pretty_name: WOS Hierarchical Multi-Label Text Classification
+size_categories:
+- 10K<n<100K
+---
 Introduced by du Toit and Dunaiski (2024) [Introducing Three New Benchmark Datasets for Hierarchical Text Classification](https://arxiv.org/abs/2411.19119).
 
 The WOS Hierarchical Text Classification are three dataset variants created from Web of Science (WOS) title and abstract data categorised into a hierarchical, multi-label class structure. The aim of the sampling and filtering methodology used was to create well-balanced class distributions (at chosen hierarchical levels). Furthermore, the WOS_JTF variant was also created with the aim to only contain publication data such that their class assignments results is classes instances that semantically more similar.
@@ -22,4 +22,28 @@ The WOS Hierarchical Text Classification are three dataset variants created from
 The three dataset variants have the following properties:  
 1. WOS_JT comprises 43,366 total samples (train=30356, dev=6505, test=6505) and only uses the journal-based classifications as labels.
 2. WOS_CT comprises 65,200 total samples (train=45640, dev=9780, test=9780) and only uses citation-based classifications as labels.
-3. WOS_JTF comprises 42,926 total samples (train=30048, dev=6439, test=6439) and uses a filtered set of papers based on journal and citation classification.
+3. WOS_JTF comprises 42,926 total samples (train=30048, dev=6439, test=6439) and uses a filtered set of papers based on journal and citation classification.
+
+Dataset details:
+
+*.json:  
+ - concatenated title and abstract mapped to a list each associated class label.  
+
+depth2label.pt: dictionary where:  
+ - key = depth of classification hierarchy.  
+ - value = list of classes associated with depth.  
+
+path_list.pt:  
+ - list of tuples for every edge between classes in the hierarchical classification. This specifies the acyclic graph.  
+
+slot.pt: dictionary where:  
+ - key = label_id of parent class.  
+ - value = label_ids of children classes.  
+
+value2slot.pt: dictionary where:  
+ - key = label_id.  
+ - value = label_id of parent class.  
+
+value_dict.pt: dictionary where:  
+ - key = label_id.  
+ - value = string representation of class.