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README.md
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- source_sentence: Cats usually hate dogs.
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sentences:
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- Куда вы ходили в прошлое воскресенье?
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-
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- Mir tut der Arm weh.
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- source_sentence: How foolish I was not to discover that simple lie!
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sentences:
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- Το σχολείο μας έχει εννιά τάξεις.
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- When applying to American universities, your TOEFL score is only one factor.
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- Je n'ai pas encore pris ma décision.
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---
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# SentenceTransformer based on agentlans/multilingual-e5-small-aligned
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [agentlans/multilingual-e5-small-aligned](https://huggingface.co/agentlans/multilingual-e5-small-aligned). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
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- One of the smallest multilingual embedding models on Huggingface
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- Includes pairs where one or both sentences are non-English
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- For each pair, two negative examples were generated
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- source_sentence: Cats usually hate dogs.
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sentences:
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- Куда вы ходили в прошлое воскресенье?
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- >-
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The bottles of beer that I brought to the party were redundant; the host's
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family owned a brewery.
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- Mir tut der Arm weh.
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- source_sentence: How foolish I was not to discover that simple lie!
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sentences:
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- Το σχολείο μας έχει εννιά τάξεις.
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- When applying to American universities, your TOEFL score is only one factor.
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- Je n'ai pas encore pris ma décision.
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license: mit
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---
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# SentenceTransformer based on agentlans/multilingual-e5-small-aligned
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [agentlans/multilingual-e5-small-aligned](https://huggingface.co/agentlans/multilingual-e5-small-aligned). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
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- One of the smallest multilingual embedding models on Huggingface
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- This model is aligned which means translations have similar embeddings compared to unrelated sentences
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- Finetuned on 1,000,000 randomly selected sentence pairs downloaded from Tatoeba 2024-09-26
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- Includes pairs where one or both sentences are non-English
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- For each pair, two negative examples were generated
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