Link to AWED-FiNER paper and GitHub repository, add sample usage

Hi! I'm Niels, part of the community science team at Hugging Face.

This PR improves the dataset card for CLASSER by:
- Linking it to the associated AWED-FiNER paper (https://huggingface.co/papers/2601.10161).
- Adding a link to the official GitHub repository.
- Adding a link to the interactive Hugging Face Space demo.
- Including a sample usage section with a code snippet found in the GitHub repository.
- Updating the citation section to include the AWED-FiNER paper.
- Adding the `other` task category to the metadata.

README.md

@@ -1,21 +1,24 @@
 ---
-license: mit
-task_categories:
-- token-classification
 language:
 - as
 - brx
 - mr
 - ne
 - sa
-pretty_name: CLASSER
+license: mit
 size_categories:
 - 10M<n<100M
+task_categories:
+- token-classification
+- other
+pretty_name: CLASSER
 ---
 
-# [CLASSER: Cross-lingual Annotation Projection enhancement through Script Similarity for Fine-grained Named Entity Recognition](https://huggingface.co/datasets/prachuryyaIITG/CLASSER)
+# CLASSER: Cross-lingual Annotation Projection enhancement through Script Similarity for Fine-grained Named Entity Recognition
+
+**CLASSER** is a framework for cross-lingual annotation projection with script-similarity-based refinement to create high-quality fine-grained named entity recognition datasets. It is part of the [AWED-FiNER](https://github.com/PrachuryyaKaushik/AWED-FiNER) ecosystem.
 
-**CLASSER** is a framework for cross-lingual annotation projection with script-similarity-based refinement to create high-quality fine-grained named entity recognition datasets.
+[**Paper**](https://huggingface.co/papers/2601.10161) | [**GitHub**](https://github.com/PrachuryyaKaushik/AWED-FiNER) | [**Interactive Demo**](https://huggingface.co/spaces/prachuryyaIITG/AWED-FiNER)
 
 Utilizing CLASSER, fine-grained named entity recognition dataset is created in five languages: Assamese (as), Bodo (brx), Marathi (mr), Nepali (ne) and Sanskrit (sa).
 
@@ -25,6 +28,25 @@ Utilizing CLASSER, fine-grained named entity recognition dataset is created in f
 
 *Figure: Overview of the CLASSER framework.*
 
+## Sample Usage
+
+You can use the AWED-FiNER agentic tool to interact with expert models trained using this framework. Below is an example using the `smolagents` library:
+
+```python
+from smolagents import CodeAgent, HfApiModel
+from tool import AWEDFiNERTool
+
+# Initialize the expert tool
+ner_tool = AWEDFiNERTool()
+
+# Initialize the agent (using a model of your choice)
+agent = CodeAgent(tools=[ner_tool], model=HfApiModel())
+
+# The agent will automatically use AWED-FiNER for specialized NER
+# Case: Processing a vulnerable language (Bodo)
+agent.run("Recognize the named entities in this Bodo sentence: 'बिथाङा दिल्लियाव थाङो।'")
+```
+
 ## CLASSER Dataset Statistics
 
 <table>
@@ -79,9 +101,18 @@ Utilizing CLASSER, fine-grained named entity recognition dataset is created in f
 
 ## Citation
 
-If you use this dataset, please cite the following paper:
+If you use this dataset, please cite the following papers:
 
 ```bibtex
+@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       = {submit/7163987}
+}
+
 @inproceedings{kaushik2025classer,
   title     = {{CLASSER}: Cross-lingual Annotation Projection enhancement through Script Similarity for Fine-grained Named Entity Recognition},
   author    = {Kaushik, Prachuryya and Anand, Ashish},
@@ -90,3 +121,4 @@ If you use this dataset, please cite the following paper:
   publisher = {Association for Computational Linguistics},
   note      = {Main conference paper}
 }
+```