mteb/NepaliNewsClassification

Tasks

• text-classification

Citation text from the card

@inproceedings{arora-2020-inltk,
  abstract = {We present iNLTK, an open-source NLP library consisting of pre-trained language models and out-of-the-box support for Data Augmentation, Textual Similarity, Sentence Embeddings, Word Embeddings, Tokenization and Text Generation in 13 Indic Languages. By using pre-trained models from iNLTK for text classification on publicly available datasets, we significantly outperform previously reported results. On these datasets, we also show that by using pre-trained models and data augmentation from iNLTK, we can achieve more than 95{\%} of the previous best performance by using less than 10{\%} of the training data. iNLTK is already being widely used by the community and has 40,000+ downloads, 600+ stars and 100+ forks on GitHub.},
  address = {Online},
  author = {Arora, Gaurav},
  booktitle = {Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS)},
  doi = {10.18653/v1/2020.nlposs-1.10},
  editor = {Park, Eunjeong L.  and
Hagiwara, Masato  and
Milajevs, Dmitrijs  and
Liu, Nelson F.  and
Chauhan, Geeticka  and
Tan, Liling},
  month = nov,
  pages = {66--71},
  publisher = {Association for Computational Linguistics},
  title = {i{NLTK}: Natural Language Toolkit for Indic Languages},
  url = {https://aclanthology.org/2020.nlposs-1.10},
  year = {2020},
}


@article{enevoldsen2025mmtebmassivemultilingualtext,
  title={MMTEB: Massive Multilingual Text Embedding Benchmark},
  author={Kenneth Enevoldsen and Isaac Chung and Imen

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