Add library_name and improve documentation

README.md

@@ -1,24 +1,27 @@
 ---
-license: mit
+base_model:
+- FacebookAI/xlm-roberta-large
 datasets:
 - MultiCoNER/multiconer_v2
 language:
 - it
+license: mit
 metrics:
 - f1
 - precision
 - recall
-base_model:
-- FacebookAI/xlm-roberta-large
 pipeline_tag: token-classification
 tags:
 - NER
 - Named_Entity_Recognition
 pretty_name: MultiCoNER2 Italian XLM-RoBERTa
+library_name: transformers
 ---
 
 **XLM-RoBERTa is fine-tuned on Italian [MultiCoNER2](https://huggingface.co/datasets/MultiCoNER/multiconer_v2) dataset for Fine-grained Named Entity Recognition.**
 
+This model is an expert detector part of the **AWED-FiNER** collection, as 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
@@ -71,6 +74,7 @@ If you use this model, please cite the following papers:
 ```bibtex
 @inproceedings{fetahu2023multiconer,
   title={MultiCoNER v2: a Large Multilingual dataset for Fine-grained and Noisy Named Entity Recognition},
+  author={Fetahu, Besnik}
   author={Fetahu, Besnik and Chen, Zhiyu and Kar, Sudipta and Rokhlenko, Oleg and Malmasi, Shervin},
   booktitle={Findings of the Association for Computational Linguistics: EMNLP 2023},
   pages={2027--2051},
@@ -93,4 +97,5 @@ If you use this model, please cite the following papers:
   booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
   volume={40},
   year={2026}
-}
+}
+```