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  # [bilingual-embedding-small](https://huggingface.co/Lajavaness/bilingual-embedding-small)
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- Bilingual-embedding is the Embedding Model for bilingual language: french and english. This model is a specialized sentence-embedding trained specifically for the bilingual language, leveraging the robust capabilities of [Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384), a pre-trained language model is built upon [multilingual-e5](https://huggingface.co/intfloat/multilingual-e5-small) architecture. The model utilizes MiniLM to encode english-french sentences into a 1024-dimensional vector space, facilitating a wide range of applications from semantic search to text clustering. The embeddings capture the nuanced meanings of english-french sentences, reflecting both the lexical and contextual layers of the language.
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  ## Full Model Architecture
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  sentences = ["Paris est une capitale de la France", "Paris is a capital of France"]
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  model = SentenceTransformer('Lajavaness/bilingual-embedding-small', trust_remote_code=True)
 
 
 
 
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  print(embeddings)
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  ```
 
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  # [bilingual-embedding-small](https://huggingface.co/Lajavaness/bilingual-embedding-small)
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+ Bilingual-embedding is the Embedding Model for bilingual language: french and english. This model is a specialized sentence-embedding trained specifically for the bilingual language, leveraging the robust capabilities of [Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384), a pre-trained language model is built upon [multilingual-e5](https://huggingface.co/intfloat/multilingual-e5-small) architecture. The model utilizes MiniLM to encode english-french sentences into a 384-dimensional vector space, facilitating a wide range of applications from semantic search to text clustering. The embeddings capture the nuanced meanings of english-french sentences, reflecting both the lexical and contextual layers of the language.
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  ## Full Model Architecture
 
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  sentences = ["Paris est une capitale de la France", "Paris is a capital of France"]
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  model = SentenceTransformer('Lajavaness/bilingual-embedding-small', trust_remote_code=True)
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+ embeddings = model.encode(sentences)
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  print(embeddings)
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  ```