Add library_name to metadata

Hi! I'm Niels from the community science team at Hugging Face.

This PR adds `library_name: transformers` to the model card's metadata. This will enable the "Use in Transformers" button on the model page, helping users quickly load and use your model with the standard library.

The model card already provides excellent documentation, including performance metrics, sample usage, and links to the paper and GitHub repository!

README.md

@@ -1,16 +1,17 @@
 ---
-license: mit
+base_model:
+- google/muril-large-cased
 datasets:
 - prachuryyaIITG/CLASSER
 language:
 - ne
+license: mit
 metrics:
 - f1
 - precision
 - recall
-base_model:
-- google/muril-large-cased
 pipeline_tag: token-classification
+library_name: transformers
 tags:
 - NER
 - Named_Entity_Recognition
@@ -19,6 +20,8 @@ pretty_name: CLASSER Nepali MuRIL
 
 **MuRIL is fine-tuned on Nepali [CLASSER](https://huggingface.co/datasets/prachuryyaIITG/CLASSER) dataset for Fine-grained Named Entity Recognition.**
 
+This model is part of the work 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).
+
 The tagset of [MultiCoNER2](https://huggingface.co/datasets/MultiCoNER/multiconer_v2) is a fine-grained tagset. The fine to coarse level mapping of the tags are as follows:
 
   * Location (LOC) : Facility, OtherLOC, HumanSettlement, Station
@@ -110,4 +113,5 @@ If you use this model, please cite the following papers:
   booktitle={Findings of the Association for Computational Linguistics: EMNLP 2023},
   pages={2027--2051},
   year={2023}
-}
+}
+```