Datasets:
metadata
language:
- as
- brx
- mr
- ne
- sa
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
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).
CLASSER Framework Overview
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:
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
| 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 |
Note: IAA (Inter-Annotator Agreement) scores are represented using Cohen's κ.
Citation
If you use this dataset, please cite the following papers:
@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},
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}
}