Update model card: add library_name, pipeline_tag, and project links

README.md

@@ -1,26 +1,28 @@
 ---
-license: mit
-language:
-- en
 base_model:
 - google/muril-large-cased
-pipeline_tag: token-classification
-tags:
-- NER
-- Named_Entity_Recognition
-pretty_name: FewNERD English MuRIL
 datasets:
 - DFKI-SLT/few-nerd
+language:
+- en
+license: mit
 metrics:
 - f1
 - precision
 - recall
+pipeline_tag: other
+tags:
+- NER
+- Named_Entity_Recognition
+pretty_name: FewNERD English MuRIL
+library_name: transformers
 ---
 
-**MuRIL is fine-tuned on English [Few-NERD](https://huggingface.co/datasets/DFKI-SLT/few-nerd) dataset.** <br>
+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.
 
-Read the papers: [FewNERD in ACL-2021](https://aclanthology.org/2021.acl-long.248.pdf) , [SampurNER in AAAI-2026](https://github.com/PrachuryyaKaushik/SampurNER/blob/main/SampurNER_AAAI_extended.pdf)
+**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://github.com/PrachuryyaKaushik/SampurNER/blob/main/SampurNER_AAAI_extended.pdf)
 
 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:
 
@@ -33,7 +35,6 @@ The tagset of [Few-NERD](https://aclanthology.org/2021.acl-long.248.pdf) is a fi
   * Event : Attack, Election, Natural Disaster, Protest, Sports Event, Other
   * Misc : Astronomy, Award, Biology, Chemistry, Currency, Disease, Educational Degree, God, Language, Law, Living Thing, Medical
 
-
 ## Model performance:
 Precision: 66.21 <br>
 Recall: 69.98 <br>
@@ -92,7 +93,8 @@ If you use this model, please cite the following papers:
   year         = {2026},
   note         = {arXiv preprint, submitted},
   archivePrefix= {arXiv},
-  eprint       = {submit/7163987}
+  eprint       = {2601.10161},
+  url          = {https://arxiv.org/abs/2601.10161}
 }
 
 @inproceedings{kaushik2026sampurner,
@@ -102,3 +104,4 @@ If you use this model, please cite the following papers:
   volume={40},
   year={2026}
 }
+```