Update README.md

README.md

@@ -19,15 +19,17 @@ Founded in 2019, Anvilogic specializes in AI-driven threat detection and automat
 
 ### Models
 
-- **Embedder :** This model provides representation for domain names. This is used to mine similar domain.  This model exists both based on RoBerta model (with BPE tokenization) and CANINE-c (with character-level encoding)
-- **Cross-Encoder :** This model is able to compare two domain names and conclude if one model is a typosquat of another.  This model exists both based on RoBerta model (with BPE tokenization) and CANINE-c (with character-level encoding)
-- **T5 Detection :** This model is a derived version of T5 trained on a new task. with the prefix : "Is the first domain a typosquat of the second : " to which we append *typosquat candidate domain* and *Legitimate domain*
+- **Embedders :** This model provides representation for domain names. This is used to mine similar domains.  This model exists both based on RoBERTa model (with BPE tokenization) and CANINE-c (with character-level encoding)
+- **Cross-Encoders :** This model is able to compare two domain names and conclude if one domain is a typosquat of another.  This model exists both based on RoBERTa model (with BPE tokenization) and CANINE-c (with character-level encoding)
+- **T5 :** This model is a derived version of T5 trained on a new task, with the prefix : "Is the first domain a typosquat of the second : " to which we append *TYPOSQUAT_DOMAIN* and *LEGITIMATE_DOMAIN*
 
 ### Datasets
 
 - **Embedder training dataset :** Dataset formatted to train embedding model with (Anchor,Positive) pairs
-- **Cross-Encoder :** Dataset formatted to train Cross-encoder model with (Anchor,Positive,label) samples.
-- **T5 Detection :** Dataset formatted to train T5 model with (prompt,response) pairs .
+- **Cross-Encoder training dataset :** Dataset formatted to train Cross-encoder model with (Anchor,Positive,label) samples.
+- **T5 training dataset :** Dataset formatted to train T5 model with (prompt,response) pairs .
 
 ### Spaces
-  Multiple spaces are provided to try aforementioned models.
+- **Embedder Typosquat Detect :** Allows the user to retrieve most similar domains from a pool of 4000 most common domains.
+- **CE Typosquat Detect :** Allows the user to compare two domains using Cross-encoders.The model outputs of a probability of typosquatting.
+- **T5 Typosquat Detect :** Allows the user to compare two domains using T5. The model outputs a boolean.