Papers by Sulav Timilsina
NepBERTa: Nepali Language Model Trained in a Large Corpus (2022.aacl-short)
Copied to clipboard
| Challenge: | Nepali is a low-resource language with more than 40 million speakers worldwide. |
| Approach: | They present a BERT-based natural language understanding model trained on the most extensive monolingual Nepali corpus ever. |
| Outcome: | The proposed model performs well in Nepali-specific NLP tasks including Named-Entity Recognition, Content Classification, POS Tagging, and Sequence Pair Similarity. |