Update README.md

README.md

@@ -12,7 +12,7 @@ language:
 - en
 ---
 
-# Hierarchy-Transformers/HiT-MiniLM-WordNetNoun
+# Hierarchy-Transformers/HiT-MiniLM-L12-WordNetNoun
 
 A **Hi**erarchy **T**ransformer Encoder (HiT) model that explicitly encodes entities according to their hierarchical relationships.
 
@@ -20,7 +20,7 @@ A **Hi**erarchy **T**ransformer Encoder (HiT) model that explicitly encodes enti
 
 <!-- Provide a longer summary of what this model is. -->
 
-HiT-MiniLM-WordNet is a HiT model trained on WordNet's noun hierarchy with random negative sampling. 
+HiT-MiniLM-L12-WordNet is a HiT model trained on WordNet's noun hierarchy with random negative sampling.
 
 - **Developed by:** [Yuan He](https://www.yuanhe.wiki/), Zhangdie Yuan, Jiaoyan Chen, and Ian Horrocks
 - **Model type:** Hierarchy Transformer Encoder (HiT)
@@ -37,13 +37,15 @@ HiT-MiniLM-WordNet is a HiT model trained on WordNet's noun hierarchy with rando
 - **Repository:** https://github.com/KRR-Oxford/HierarchyTransformers
 - **Paper:** [Language Models as Hierarchy Encoders](tbd)
 
-## Uses
+## Usage
 
 <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
 HiT models are used to encode entities (presented as texts) and predict their hierarhical relationships in hyperbolic space. 
 
-## Usage
+### Get Started
+
+Install `hierarchy_transformers` (check our [repository](https://github.com/KRR-Oxford/HierarchyTransformers)) through `pip` or `GitHub`.
 
 Use the code below to get started with the model.
 
@@ -65,6 +67,8 @@ entity_names = ["computer", "personal computer", "fruit", "berry"]
 entity_embeddings = model.encode(entity_names)
 ```
 
+### Default Probing for Subsumption Prediction
+
 Use the entity embeddings to predict the subsumption relationships between them.
 
 ```python