natasha/navec
Compact high quality word embeddings for Russian language
Navec provides compact, high-quality word embeddings for the Russian language. It helps developers working with Russian text data by converting words into numerical representations, which can then be used in various natural language processing tasks. This allows for faster loading and less memory usage compared to other models, benefiting those building applications that process or analyze Russian text.
216 stars. Used by 2 other packages. No commits in the last 6 months. Available on PyPI.
Use this if you are developing applications that need to process or understand Russian text efficiently and require accurate word representations while minimizing model size and load times.
Not ideal if your application requires extremely fast individual word lookups, as Navec involves a small amount of extra computation compared to simpler word2vec models.
Stars
216
Forks
20
Language
Python
License
MIT
Category
Last pushed
Jul 24, 2023
Commits (30d)
0
Dependencies
1
Reverse dependents
2
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/embeddings/natasha/navec"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related tools
shibing624/text2vec
text2vec, text to vector....
predict-idlab/pyRDF2Vec
đ Python Implementation and Extension of RDF2Vec
IntuitionEngineeringTeam/chars2vec
Character-based word embeddings model based on RNN for handling real world texts
IITH-Compilers/IR2Vec
Implementation of IR2Vec, LLVM IR Based Scalable Program Embeddings
ddangelov/Top2Vec
Top2Vec learns jointly embedded topic, document and word vectors.