Add library_name and project links

README.md

@@ -1,57 +1,65 @@
 ---
-license: mit
-language:
-- brx
 base_model:
 - google/muril-large-cased
-pipeline_tag: token-classification
-tags:
-- NER
-- Named_Entity_Recognition
-pretty_name: CLASSER Bodo MuRIL
 datasets:
 - prachuryyaIITG/CLASSER
+language:
+- brx
+license: mit
 metrics:
 - f1
 - precision
 - recall
+pipeline_tag: token-classification
+library_name: transformers
+tags:
+- NER
+- Named_Entity_Recognition
+pretty_name: CLASSER Bodo MuRIL
 ---
 
 **MuRIL is fine-tuned on Bodo [CLASSER](https://huggingface.co/datasets/prachuryyaIITG/CLASSER) dataset for Fine-grained Named Entity Recognition.**
 
-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:
+This model is 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).
+
+- **GitHub Repository:** [AWED-FiNER](https://github.com/PrachuryyaKaushik/AWED-FiNER)
+- **Interactive Demo:** [AWED-FiNER Space](https://huggingface.co/spaces/prachuryyaIITG/AWED-FiNER)
 
-  * Location (LOC) : Facility, OtherLOC, HumanSettlement, Station
-  * Creative Work (CW) : VisualWork, MusicalWork, WrittenWork, ArtWork, Software
-  * Group (GRP) : MusicalGRP, PublicCORP, PrivateCORP, AerospaceManufacturer, SportsGRP, CarManufacturer, ORG
-  * Person (PER) : Scientist, Artist, Athlete, Politician, Cleric, SportsManager, OtherPER
-  * Product (PROD) : Clothing, Vehicle, Food, Drink, OtherPROD
-  * Medical (MED) : Medication/Vaccine, MedicalProcedure, AnatomicalStructure, Symptom, Disease
+### Tagset Mapping
+The model uses the fine-grained tagset from [MultiCoNER2](https://huggingface.co/datasets/MultiCoNER/multiconer_v2). The mapping from fine to coarse level tags is as follows:
 
-## Model performance:
-Precision: 73.83 <br>
-Recall: 76.37 <br>
-**F1: 75.08** <br>
+*   **Location (LOC)** : Facility, OtherLOC, HumanSettlement, Station
+*   **Creative Work (CW)** : VisualWork, MusicalWork, WrittenWork, ArtWork, Software
+*   **Group (GRP)** : MusicalGRP, PublicCORP, PrivateCORP, AerospaceManufacturer, SportsGRP, CarManufacturer, ORG
+*   **Person (PER)** : Scientist, Artist, Athlete, Politician, Cleric, SportsManager, OtherPER
+*   **Product (PROD)** : Clothing, Vehicle, Food, Drink, OtherPROD
+*   **Medical (MED)** : Medication/Vaccine, MedicalProcedure, AnatomicalStructure, Symptom, Disease
 
-## Training Parameters:
-Epochs: 6 <br>
-Optimizer: AdamW <br>
-Learning Rate: 5e-5 <br>
-Weight Decay: 0.01 <br>
-Batch Size: 64 <br>
+## Model Performance
+- **Precision:** 73.83
+- **Recall:** 76.37
+- **F1 Score:** 75.08
+
+## Training Parameters
+- **Epochs:** 6
+- **Optimizer:** AdamW
+- **Learning Rate:** 5e-5
+- **Weight Decay:** 0.01
+- **Batch Size:** 64
 
 ## Contributors
-[Prachuryya Kaushik](https://www.linkedin.com/in/pkabundant/) <br>
-[Prof. Ashish Anand](https://www.linkedin.com/in/anandashish/)
+[Prachuryya Kaushik](https://www.linkedin.com/in/pkabundant/) and [Prof. Ashish Anand](https://www.linkedin.com/in/anandashish/).
 
-CLASSER is a part of the [AWED-FiNER collection](https://huggingface.co/collections/prachuryyaIITG/awed-finer). Please check: [**Paper**](https://huggingface.co/papers/2601.10161) | [**Agentic Tool**](https://github.com/PrachuryyaKaushik/AWED-FiNER) | [**Interactive Demo**](https://huggingface.co/spaces/prachuryyaIITG/AWED-FiNER)
+CLASSER is a part of the [AWED-FiNER collection](https://huggingface.co/collections/prachuryyaIITG/awed-finer).
 
 ## Sample Usage
 
 The AWED-FiNER agentic tool can be used to interact with expert models trained using this framework. Below is an example:
+
 ```bash
 pip install smolagents gradio_client
 ```
+
 ```python
 from tool import AWEDFiNERTool
 
@@ -60,8 +68,8 @@ tool = AWEDFiNERTool(
 )
 
 result = tool.forward(
-    text="Jude Bellingham joined Real Madrid in 2023.",
-    language="English"
+    text="अमिताभ बच्चनआ सासे मुंदांखा फावखुंगुर।",
+    language="Bodo"
 )
 
 print(result)
@@ -72,6 +80,16 @@ print(result)
 If you use this model, please cite the following papers:
 
 ```bibtex
+@misc{kaushik2026awedfineragentswebapplications,
+      title={AWED-FiNER: Agents, Web applications, and Expert Detectors for 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{kaushik-anand-2025-classer,
     title = "{CLASSER}: Cross-lingual Annotation Projection enhancement through Script Similarity for Fine-grained Named Entity Recognition",
     author = "Kaushik, Prachuryya  and
@@ -85,29 +103,4 @@ If you use this model, please cite the following papers:
     pages = "1745--1760",
     ISBN = "979-8-89176-298-5",
 }
-
-@misc{kaushik2026awedfineragentswebapplications,
-      title={AWED-FiNER: Agents, Web applications, and Expert Detectors for 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,
-  title={SampurNER: Fine-grained Named Entity Recognition Dataset for 22 Indian Languages},
-  author={Kaushik, Prachuryya and Anand, Ashish},
-  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
-  volume={40},
-  year={2026}
-}
-
-@inproceedings{fetahu2023multiconer,
-  title={MultiCoNER v2: a Large Multilingual dataset for Fine-grained and Noisy Named Entity Recognition},
-  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},
-  year={2023}
-}
+```