---
license: mit
task_categories:
- token-classification
language:
- as
- brx
- mr
- ne
- sa
pretty_name: CLASSER
size_categories:
- 10M
*Figure: Overview of the CLASSER framework.*
## 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 paper:
```bibtex
@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}
}