prachuryyaIITG/CLASSER_Assamese_MuRIL
Token Classification • 0.5B • Updated
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text stringlengths 0 87 |
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গেমছক O |
মাৰ্ভ B-OtherPER |
এলবাৰ্ট I-OtherPER |
মেট B-SportsManager |
গুওকাছ I-SportsManager |
আৰু O |
বিল B-Athlete |
ৱালটনে I-Athlete |
বুলি O |
কোৱাৰ O |
বিপৰীতে O |
আহমদ B-Athlete |
ৰাছাদ I-Athlete |
আৰু O |
হান্না B-OtherPER |
ষ্টৰ্ম I-OtherPER |
এ O |
চাইডলাইন O |
ৰিপ'ৰ্টাৰ O |
হিচাপে O |
কাম O |
কৰিছিল O |
বব B-Athlete |
কুইক I-Athlete |
প্ৰাক্তন O |
পেছাদাৰী O |
বাস্কেটবল O |
খেলুৱৈ O |
হিটলাৰ B-Politician |
আৰু O |
ছিজলা B-Artist |
ক O |
একেটা O |
ব্ৰেকেটত O |
ৰখাটো O |
বৰ্ণবাদী O |
আৰু O |
মাত্ৰ O |
দেখুৱাই O |
যে O |
তেওঁ O |
কিমান O |
দূৰলৈ O |
যাবলৈ O |
সাজু O |
নাগাৰামে B-VisualWork |
নন্দি I-VisualWork |
চহৰখনলৈ O |
ধন্যবাদ O |
১৯৭৫ O |
চনৰ O |
পৰা O |
১৯৭৬ O |
চনলৈকে O |
তেওঁলোক O |
মিনেছ'টা B-SportsGRP |
টুইনছ I-SportsGRP |
আৰু O |
ছান B-SportsGRP |
ডিয়েগো I-SportsGRP |
পেড্ৰেছ I-SportsGRP |
দুয়োটা O |
দলৰ O |
সৈতে O |
জড়িত O |
আছিল O |
তাৰ O |
পিছত O |
প্ৰকাশ O |
পায় O |
যে O |
মিনি B-Vehicle |
কুপাৰ I-Vehicle |
প্ৰকৃত O |
সোণৰ O |
পৰাই O |
তৈয়াৰ O |
কৰা O |
হৈছিল O |
ত্ৰি-ৰাজ্যিক B-OtherLOC |
যুদ্ধ I-OtherLOC |
চৰাই I-OtherLOC |
সংগ্ৰহালয় I-OtherLOC |
ক্লেৰমন্ট B-Station |
কাউন্টি I-Station |
বিমানবন্দৰ I-Station |
ত O |
অৱস্থিত O |
ছবিখনৰ O |
এটা O |
অংশ O |
১২ B-VisualWork |
বছৰ I-VisualWork |
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 ecosystem: Paper | GitHub | Interactive Demo
Utilizing CLASSER, fine-grained named entity recognition dataset is created in five languages: Assamese (as), Bodo (brx), Marathi (mr), Nepali (ne) and Sanskrit (sa).
Figure: Overview of the CLASSER framework.
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:
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।'")
| Language | Train set | Development set | Test set | |||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Sentences | Entities | Tokens | Sentences | Entities | Tokens | Sentences | Entities | Tokens | IAA (κ) | |
| Assamese (as) | 140,257 | 204,611 | 1,972,697 | 15,585 | 15,763 | 219,114 | 1,000 | 1,407 | 14,270 | 0.901 |
| Bodo (brx) | 212,835 | 302,713 | 2,958,455 | 23,649 | 33,808 | 329,145 | 1,000 | 1,423 | 14,082 | 0.875 |
| Marathi (mr) | 611,902 | 889,217 | 8,135,813 | 67,990 | 97,943 | 948,020 | 1,000 | 1,443 | 13,996 | 0.887 |
| Nepali (ne) | 414,561 | 617,957 | 5,531,683 | 46,062 | 64,098 | 642,489 | 1,000 | 1,436 | 14,142 | 0.882 |
| Sanskrit (sa) | 265,114 | 378,287 | 3,488,871 | 29,458 | 40,589 | 377,306 | 1,000 | 1,412 | 12,925 | 0.861 |
If you use this dataset, please cite the following papers:
@inproceedings{kaushik2025classer,
title = {{CLASSER}: Cross-lingual Annotation Projection enhancement through Script Similarity for Fine-grained Named Entity Recognition},
author = {Kaushik, Prachuryya and Anand, Ashish},
booktitle = {Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics},
year = {2025},
publisher = {Association for Computational Linguistics},
note = {Main conference paper}
}
@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}
}
@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},
}