Datasets
Open Access
300-Dimensional Word Embeddings for Nepali Language
- Citation Author(s):
- Submitted by:
- Rabindra Lamsal
- Last updated:
- Sat, 03/13/2021 - 00:23
- DOI:
- 10.21227/dz6s-my90
- Data Format:
- Links:
- License:
2524 Views- Categories:
- Keywords:
1 rating - Please login to submit your rating.
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.
Instructions:
from gensim.models import KeyedVectors
# Load vectors
model = KeyedVectors.load_word2vec_format(''.../path/to/nepali_embeddings_word2vec.txt', binary=False)
# find similarity between words
model.similarity('फेसबूक','इन्स्टाग्राम')
#most similar words
model.most_similar('ठमेल')
#try some linear algebra maths with Nepali words
model.most_similar(positive=['', ''], negative=[''], topn=1)Dataset Files
nepali_embeddings_word2vec.txt (1.75 GB)
Open Access dataset files are accessible to all logged in users. Don't have a login? Create a free IEEE account. IEEE Membership is not required.
Documentation
| Attachment | Size |
|---|---|
 Readme file | 141.45 KB |
