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README.md

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 # **Anvilogic - Where AI Meets Cybersecurity**
 
-Welcome to the official Hugging Face organization for Anvilogic's advanced cybersecurity AI models! Founded in 2019, Anvilogic specializes in AI-driven threat detection and automation, enhancing Security Operations Center (SOC) capabilities with scalable, data-driven solutions.
+Welcome to the official Hugging Face organization for Anvilogic's advanced cybersecurity AI models!  
+Founded in 2019, Anvilogic specializes in AI-driven threat detection and automation, enhancing Security Operations Center (SOC) capabilities with scalable, data-driven solutions.
 
-## Model collections:
+## Typosquatting collection
+  Typosquatting is a form of cyber attack where malicious actors create fake domain names that are visually or phonetically similar to legitimate domains, intending to deceive users into visiting these sites.
+  This collection aims at detecting typosquatted domains by identifying and flagging such domains :
+  It is comprised of the following:
 
-### Typosquatting collection:
-This collection is comprised of :
+### Models
 
-- **Embedder** This model provide representation for domain names. This is used to mine similar domain . 
+- **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*
+
+### 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 .
+
+### Spaces
+  Multiple spaces are provided to try aforementioned models.