Update README.md

Overview
ProcessVenue’s annotation team delivered a high-reliability SMS dataset built on 5,284 authentic messages, validated across three independent question sets through a strict double-blind dual-label workflow. Even with noisy, abbreviated SMS language, the project maintained strong label discipline using priority rules and clear tiebreakers. The outcome is a dependable 3-class corpus suitable for supervised SMS filtering, intent routing, and risk triage.

Project Purpose: This internal initiative aimed to create robust ground truth reflecting real-life SMS content—short, informal, and often ambiguous. The dataset enables systems to correctly separate:

SPAM — unsolicited promotions, scams, prize traps
PERSONAL SOCIAL — human conversation and casual texting
SERVICE ALERTS — legitimate transactional or utility notifications
A second-layer Risk Level label was included to support downstream message safety and prioritization.

Acknowledgements
We gratefully acknowledge the authors of the SMS Spam Collection dataset and the UCI Machine Learning Repository for making this dataset publicly available. All credit for the original data collection and labeling belongs to the original creators.

Dataset Source
Original dataset: **SMS Spam Collection (UCI ML Repository)**
Source URL: https://archive.ics.uci.edu/dataset/228/sms+spam+collection

Citation
Almeida, T. A., Gómez Hidalgo, J. M., & Yamakami, A. (2011). *Contributions to the Study of SMS Spam Filtering: New Collection and Results.* Proceedings of the 2011 ACM Symposium on Document Engineering (DOCENG ’11), ACM.

README.md

@@ -10,7 +10,7 @@ tags:
 - SPAM_Detection
 - Natural_Language_Processing
 - NLP
-pretty_name: SMS Communication Classifier
+pretty_name: SMS_ANNOTATION_V1
 size_categories:
 - 1K<n<10K
 ---