---
language:
- fa
library_name: hezar
tags:
- feature-extraction
- hezar
pipeline_tag: feature-extraction
---
This is the Persian word2vec embedding model trained with skipgram algorithm on the wikipedia data.

In order to use this model in Hezar you can simply use this piece of code:
```bash
pip install hezar
```
```python
from hezar.embeddings import Embedding

w2v = Embedding.load("hezarai/word2vec-skipgram-fa-wikipedia")
# Get embedding vector
vector = w2v("هزار")
# Find the word that doesn't match with the rest
doesnt_match = w2v.doesnt_match(["خانه", "اتاق", "ماشین"])
# Find the top-n most similar words to the given word
most_similar = w2v.most_similar("هزار", top_n=5)
# Find the cosine similarity value between two words
similarity = w2v.similarity("مهندس", "دکتر")
```