Sentence Similarity
sentence-transformers
Safetensors
Transformers
French
English
bilingual
feature-extraction
sentence-embedding
mteb
custom_code
Eval Results (legacy)
Instructions to use Lajavaness/bilingual-embedding-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Lajavaness/bilingual-embedding-small with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Lajavaness/bilingual-embedding-small", trust_remote_code=True) sentences = [ "C'est une personne heureuse", "C'est un chien heureux", "C'est une personne très heureuse", "Aujourd'hui est une journée ensoleillée" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use Lajavaness/bilingual-embedding-small with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Lajavaness/bilingual-embedding-small", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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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
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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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```
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