linked to blog post

README.md

@@ -11,7 +11,7 @@ library_name: transformers
 
 ## Model Description
 We present a PII detection classification model that can be used for identifying attempts to share or solicit personally identifiable information in text. The model has been fine-tuned on the [XLM-RoBERTa-Large](https://huggingface.co/xlm-roberta-large) architecture for [multilingual support](https://arxiv.org/pdf/1911.02116).
-All the model training has been conducted with Roblox anonymized internal text datasets labeled by experts and AI generated conversations. More details can be found in the [tech blog] post.
+All the model training has been conducted with Roblox anonymized internal text datasets labeled by experts and AI generated conversations. More details can be found in the [Tech Blog Post].
 
 The model classifies text into two PII-related categories in a multi-label fashion. The class labels are as follows: `privacy_asking_for_pii` and `privacy_giving_pii`. 
 - **PRIVACY_ASKING_FOR_PII**: Attempting to obtain personal identifying information (PII) through direct questions or insinuation.
@@ -30,7 +30,7 @@ The table below displays evaluation metrics on internal held-out datasets and co
 |NemoGuard 8B|49.82%|3.26%|
 |Piiranha NER|97.09%|33.20%|
 
-It is worth noting that the model is specifically designed to understand context and detect adversarial patterns where users attempt to bypass filters through creative spelling, character substitution, or implicit references. It focus on conversational context of asking/sharing PII rather than traditional focus on named-entity recognition, and is especially good at detecting subtle attempts to solicit or share PII even when explicit personal information is not present in the text.
+It is worth noting that the model is specifically designed to understand context and detect adversarial patterns where users attempt to bypass filters through creative spelling, character substitution, or implicit references. It focus on conversational context of asking/sharing PII rather than traditional focus on named-entity recognition, and is especially good at detecting subtle attempts to solicit or share PII even when explicit personal information is not present in the text. More technical details can be found in the Tech Blog Post.
 
 
 ## Usage