Update README.md

README.md

@@ -43,7 +43,7 @@ datasets:
 [![Data](https://img.shields.io/badge/🤗%20Training%20Data-Ecomniverse-yellow)](https://huggingface.co/datasets/thebajajra/Ecom-niverse)
 [![GitHub](https://img.shields.io/badge/GitHub-Code-blue)](https://github.com/bajajra/RexBERT)
 
-> **TL;DR**: An encoder-only transformer (BERT-style) for **e-commerce** applications, trained in three phases—**Pre-training**, **Context Extension**, and **Decay**—to power product search, attribute extraction, classification, and embeddings use cases. The model has been trained on 2.3T+ tokens along with 350B+ e-commerce-specific tokens
+> **TL;DR**: An encoder-only transformer (ModernBERT-style) for **e-commerce** applications, trained in three phases—**Pre-training**, **Context Extension**, and **Decay**—to power product search, attribute extraction, classification, and embeddings use cases. The model has been trained on 2.3T+ tokens along with 350B+ e-commerce-specific tokens
 
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@@ -220,7 +220,7 @@ trainer.train()
 
 ## Model Architecture & Compatibility
 
-- **Architecture:** Encoder-only, BERT-style **base** model.  
+- **Architecture:** Encoder-only, ModernBERT-style **base** model.  
 - **Libraries:** Works with **🤗 Transformers**; supports **fill-mask** and **feature-extraction** pipelines.  
 - **Context length:** Increased during the **Context Extension** phase—ensure `max_position_embeddings` in `config.json` matches your desired max length.  
 - **Files:** `config.json`, tokenizer files, and (optionally) heads for MLM or classification.  
@@ -246,19 +246,4 @@ trainer.train()
 
 - **Author/maintainer:** [Rahul Bajaj](https://huggingface.co/thebajajra) 
 
----
-
-## Citation
-
-If you use RexBERT-base in your work, please cite it:
-
-```bibtex
-@software{rexbert_base_2025,
-  title        = {RexBERT-base: An e-commerce domain encoder},
-  author       = {Bajajra, Rahul Bajaj},
-  year         = {2025},
-  url          = {https://huggingface.co/thebajajra/RexBERT-base}
-}
-```
-
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