Update README.md

README.md

@@ -18,11 +18,9 @@ pretty_name: FewNERD English MuRIL
 library_name: transformers
 ---
 
-This model is an expert detector for Fine-grained Named Entity Recognition (FgNER) within the **AWED-FiNER** project. It is a fine-tuned version of `google/muril-large-cased` on the English [Few-NERD](https://huggingface.co/datasets/DFKI-SLT/few-nerd) dataset.
+This model is a fine-tuned version of `google/muril-large-cased` on the English [Few-NERD](https://huggingface.co/datasets/DFKI-SLT/few-nerd) dataset.
 
-**AWED-FiNER** is presented in the paper: [AWED-FiNER: Agents, Web applications, and Expert Detectors for Fine-grained Named Entity Recognition across 36 Languages for 6.6 Billion Speakers](https://huggingface.co/papers/2601.10161).
-
-Read the papers: [FewNERD in ACL-2021](https://aclanthology.org/2021.acl-long.248.pdf) , [SampurNER in AAAI-2026](https://ojs.aaai.org/index.php/AAAI/article/view/40405)
+It is part of the [AWED-PIPER](https://github.com/PrachuryyaKaushik/AWED-PIPER) ecosystem: [**Paper**](https://huggingface.co/papers/2601.10161) | [**Agent for FgNER**](https://github.com/PrachuryyaKaushik/AWED-FiNER) | [**Web App for FgNER**](https://huggingface.co/spaces/prachuryyaIITG/AWED-FiNER) | [**Agent for PII Protection**](https://github.com/PrachuryyaKaushik/AWED-PIPER) | [**Web App for PII Protection**](https://huggingface.co/spaces/prachuryyaIITG/AWED_PII_Protector)
 
 The tagset of [Few-NERD](https://aclanthology.org/2021.acl-long.248.pdf) is a fine-grained tagset. The fine to coarse level mapping of the tags are as follows:
 
@@ -47,8 +45,6 @@ Learning Rate: 5e-5 <br>
 Weight Decay: 0.01 <br>
 Batch Size: 64 <br>
 
-[**AWED-FiNER collection**](https://huggingface.co/collections/prachuryyaIITG/awed-finer) | [**Paper**](https://huggingface.co/papers/2601.10161) | [**Agentic Tool**](https://github.com/PrachuryyaKaushik/AWED-FiNER) | [**Interactive Demo**](https://huggingface.co/spaces/prachuryyaIITG/AWED-FiNER)
-
 ## Sample Usage of Agentic Tool
 
 The AWED-FiNER agentic tool can be used to interact with expert models trained using this framework. Below is an example:
@@ -87,14 +83,14 @@ If you use this model, please cite the following papers:
     pages = "3198--3213",
 }
 
-@misc{kaushik2026awedfiner,
-  title        = {AWED-FiNER: Agents, Web Applications, and Expert Detectors for Fine-grained Named Entity Recognition across 36 Languages for 6.6 Billion Speakers},
-  author       = {Kaushik, Prachuryya and Anand, Ashish},
-  year         = {2026},
-  note         = {arXiv preprint, submitted},
-  archivePrefix= {arXiv},
-  eprint       = {2601.10161},
-  url          = {https://arxiv.org/abs/2601.10161}
+@misc{kaushik2026awedpiperagentswebapplications,
+      title={AWED-PIPER: Agents, Web Applications & Expert Detectors for Personally Identifiable Information Protection & Fine-grained Named Entity Recognition across 36 languages for 6.6 Billion Speakers}, 
+      author={Prachuryya Kaushik and Ashish Anand},
+      year={2026},
+      eprint={2601.10161},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2601.10161}, 
 }
 
 @inproceedings{kaushik2026sampurner,