300-Dimensional Word Embeddings for Nepali Language

- Citation Author(s):
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Rabindra Lamsal (Artificial Intelligence & Data Science Lab, SC&SS, JNU)
- Submitted by:
- Rabindra Lamsal
- Last updated:
- DOI:
- 10.21227/dz6s-my90
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Abstract
This pre-trained Word2Vec model has 300-dimensional vectors for more than 0.5 million Nepali words and phrases. A separate Nepali language text corpus was created using the news contents freely available in the public domain. The text corpus contained more than 90 million running words. The "Nepali Text Corpus" can be accessed freely from http://dx.doi.org/10.21227/jxrd-d245.
Word2Vec model details: Embeddings Dimension: 300, Architecture: Continuous - BOW, Training algorithm: Negative sampling = 15, Context (window) size: 10, Token minimum count: 2, Encoded in UTF-8.