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--- |
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language: |
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- fa |
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library_name: hezar |
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tags: |
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- feature-extraction |
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- hezar |
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pipeline_tag: feature-extraction |
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--- |
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This is the Persian word2vec embedding model trained with CBOW algorithm on the wikipedia data. |
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In order to use this model in Hezar you can simply use this piece of code: |
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```bash |
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pip install hezar |
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``` |
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```python |
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from hezar.embeddings import Embedding |
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w2v = Embedding.load("hezarai/word2vec-cbow-fa-wikipedia") |
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# Get embedding vector |
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vector = w2v("هزار") |
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# Find the word that doesn't match with the rest |
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doesnt_match = w2v.doesnt_match(["خانه", "اتاق", "ماشین"]) |
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# Find the top-n most similar words to the given word |
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most_similar = w2v.most_similar("هزار", top_n=5) |
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# Find the cosine similarity value between two words |
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similarity = w2v.similarity("مهندس", "دکتر") |
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``` |
