Update README.md

README.md

@@ -13,14 +13,19 @@ task_categories:
 - other
 pretty_name: CLASSER
 ---
+# CLASSER: Cross-lingual Annotation Projection enhancement through Script Similarity for Fine-grained Named Entity Recognition
 
-# APTFiNER: Annotation Preserving Translation 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.
 
-**APTFiNER** is a framework to create high-quality fine-grained named entity recognition datasets through annotation preserving translation using LLMs. It is part of the [AWED-FiNER](https://github.com/PrachuryyaKaushik/AWED-FiNER) ecosystem.
+It is part of the [AWED-FiNER](https://github.com/PrachuryyaKaushik/AWED-FiNER) ecosystem: [**Paper**](https://huggingface.co/papers/2601.10161) | [**GitHub**](https://github.com/PrachuryyaKaushik/AWED-FiNER) | [**Interactive Demo**](https://huggingface.co/spaces/prachuryyaIITG/AWED-FiNER)
 
-[**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).
 
-Utilizing APTFiNER, fine-grained named entity recognition dataset is created in six languages: Assamese (as), Bodo (brx), Marathi (mr), Nepali (ne), Tamil (ta) and Telugu (te).
+## CLASSER Framework Overview
+
+<img src="CLASSER_framework.png" alt="CLASSER Framework Overview" width="450"/>
+
+*Figure: Overview of the CLASSER framework.*
 
 ## Sample Usage
 
@@ -29,19 +34,16 @@ You can use the AWED-FiNER agentic tool to interact with expert models trained u
 ```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।'")
 ```
 
-## APTFiNER Dataset Statistics
+## CLASSER Dataset Statistics
 
 <table>
 <thead>
@@ -60,67 +62,43 @@ agent.run("Recognize the named entities in this Bodo sentence: 'बिथाङ
   <tbody align="center">
     <tr>
       <td><b>Assamese (as)</b></td>
-      <td>53,160</td><td>90,489</td><td>796,912</td>
-      <td>5,848</td><td>9,959</td><td>87,693</td>
+      <td>140,257</td><td>204,611</td><td>1,972,697</td>
+      <td>15,585</td><td>15,763</td><td>219,114</td>
       <td>1,000</td><td>1,407</td><td>14,270</td><td><b>0.901</b></td>
     </tr>
     <tr>
       <td><b>Bodo (brx)</b></td>
-      <td>23,571</td><td>36,977</td><td>406,782</td>
-      <td>2,591</td><td>4,043</td><td>44,708</td>
+      <td>212,835</td><td>302,713</td><td>2,958,455</td>
+      <td>23,649</td><td>33,808</td><td>329,145</td>
       <td>1,000</td><td>1,423</td><td>14,082</td><td><b>0.875</b></td>
     </tr>
     <tr>
       <td><b>Marathi (mr)</b></td>
-      <td>97,752</td><td>172,635</td><td>1,400,010</td>
-      <td>10,753</td><td>18,993</td><td>153,982</td>
+      <td>611,902</td><td>889,217</td><td>8,135,813</td>
+      <td>67,990</td><td>97,943</td><td>948,020</td>
       <td>1,000</td><td>1,443</td><td>13,996</td><td><b>0.887</b></td>
     </tr>
     <tr>
       <td><b>Nepali (ne)</b></td>
-      <td>67,096</td><td>110,068</td><td>948,504</td>
-      <td>7,382</td><td>12,091</td><td>104,321</td>
+      <td>414,561</td><td>617,957</td><td>5,531,683</td>
+      <td>46,062</td><td>64,098</td><td>642,489</td>
       <td>1,000</td><td>1,436</td><td>14,142</td><td><b>0.882</b></td>
     </tr>
     <tr>
-      <td><b>Tamil (ta)</b></td>
-      <td>58,330</td><td>100,254</td><td>773,419</td>
-      <td>6,420</td><td>11,031</td><td>85,094</td>
-      <td>1,000</td><td>1,442</td><td>13,225</td><td><b>0.873</b></td>
-    </tr>
-    <tr>
-      <td><b>Telugu (te)</b></td>
-      <td>65,477</td><td>109,597</td><td>843,701</td>
-      <td>7,205</td><td>12,073</td><td>92,835</td>
-      <td>1,000</td><td>1,437</td><td>12,925</td><td><b>0.877</b></td>
+      <td><b>Sanskrit (sa)</b></td>
+      <td>265,114</td><td>378,287</td><td>3,488,871</td>
+      <td>29,458</td><td>40,589</td><td>377,306</td>
+      <td>1,000</td><td>1,412</td><td>12,925</td><td><b>0.861</b></td>
     </tr>
   </tbody>
 </table>
-
 *Note: IAA (Inter-Annotator Agreement) scores are represented using Cohen's κ.*
 
-## Contributors
-[Prachuryya Kaushik](https://www.linkedin.com/in/pkabundant/) <br>
-[Adittya Gupta](https://www.linkedin.com/in/adittya-gupta-b64356224/) <br>
-[Ajanta Maurya](https://www.linkedin.com/in/ajanta-maurya/) <br>
-[Gautam Sharma](https://www.linkedin.com/in/g-s01/) <br>
-[Prof. V Vijaya Saradhi](https://www.linkedin.com/in/vijaya-saradhi-a90a604/) <br>
-[Prof. Ashish Anand](https://www.linkedin.com/in/anandashish/)
-
-
 ## Citation
 
 If you use this dataset, please cite the following papers:
 
 ```bibtex
-@inproceedings{kaushik2026aptfiner,
-  title={APTFiNER: Annotation Preserving Translation for Fine-grained Named Entity Recognition},
-  author={Kaushik, Prachuryya and Gupta, Adittya and Maurya, Ajanta and Sharma, Gautam and Saradhi, Vijaya V and Anand, Ashish},
-  booktitle={Proceedings of the Fifteenth Language Resources and Evaluation Conference},
-  volume={15},
-  year={2026}
-}
-
 @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},
@@ -129,7 +107,6 @@ If you use this dataset, please cite the following papers:
   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},