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README.md
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- krc
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# TSjB/labse-
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It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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Fine-tined by [Bogdan Tewunalany](https://t.me/bogdan_tewunalany)
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from sentence_transformers import SentenceTransformer
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sentences = ["This is an example sentence", "Бу айтым юлгюдю"]
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model = SentenceTransformer('TSjB/labse-
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embeddings = model.encode(sentences)
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print(embeddings)
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```
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italian_sentences = base::c("cane", "I cuccioli sono carini.", "Mi piace fare lunghe passeggiate lungo la spiaggia con il mio cane.")
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qarachay_sentences = base::c("ит", "Итле джагъымлыдыла.", "Джагъа юсю бла итим бла айланыргъа сюеме.")
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model = st$SentenceTransformer('TSjB/labse-
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english_embeddings = model$encode(english_sentences)
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italian_embeddings = model$encode(italian_sentences)
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- krc
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---
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# TSjB/labse-qm
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It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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Fine-tined by [Bogdan Tewunalany](https://t.me/bogdan_tewunalany)
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from sentence_transformers import SentenceTransformer
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sentences = ["This is an example sentence", "Бу айтым юлгюдю"]
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model = SentenceTransformer('TSjB/labse-qm')
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embeddings = model.encode(sentences)
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print(embeddings)
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```
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italian_sentences = base::c("cane", "I cuccioli sono carini.", "Mi piace fare lunghe passeggiate lungo la spiaggia con il mio cane.")
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qarachay_sentences = base::c("ит", "Итле джагъымлыдыла.", "Джагъа юсю бла итим бла айланыргъа сюеме.")
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model = st$SentenceTransformer('TSjB/labse-qm')
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english_embeddings = model$encode(english_sentences)
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italian_embeddings = model$encode(italian_sentences)
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