Create README.md

README.md

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+---
+language:
+- fa
+pipeline_tag: feature-extraction
+---
+This is the original fasttext embedding model for Persian from [here](https://fasttext.cc/docs/en/crawl-vectors.html#models) loaded and converted using Gensim and exported to Hezar compatible format.
+For more info, see [here](https://fasttext.cc/docs/en/support.html).
+
+In order to use this model in Hezar you can simply use this piece of code:
+```bash
+pip install hezar
+```
+```python
+from hezar import Embedding
+
+fasttext = Embedding.load("hezarai/fasttext-fa-300")
+# Get embedding vector
+vector = fasttext("هزار")
+# Find the word that doesn't match with the rest
+doesnt_match = fasttext.doesnt_match(["خانه", "اتاق", "ماشین"])
+# Find the top-n most similar words to the given word
+most_similar = fasttext.most_similar("هزار", top_n=5)
+# Find the cosine similarity value between two words
+similarity = fasttext.similarity("مهندس", "دکتر")
+```