Update README.md
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README.md
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@@ -31,6 +31,8 @@ Each model is provided as a .pkl file and can be loaded using Python's pickle mo
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from sentence_transformers import SentenceTransformer
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import pickle
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model = SentenceTransformer('GiliGold/Knesset-multi-e5-large')
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embedding_vector = model.encode(sentence)
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from sentence_transformers import SentenceTransformer
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import pickle
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sentence = "זה משפט לדוגמה"
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# Convert input text into embeddings using Knesset-multi-e5-large
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model = SentenceTransformer('GiliGold/Knesset-multi-e5-large')
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embedding_vector = model.encode(sentence)
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