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LegalKit Retrieval, a binary Search with Scalar (int8) Rescoring through French legal codes is now available as a 🤗 Space.
LegalKit Retrieval, a binary Search with Scalar (int8) Rescoring through French legal codes is now available as a 🤗 Space.
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This process is designed to be memory efficient and fast, with the binary index being small enough to fit in memory and the int8 index being loaded as a view. Additionally, the binary index is much faster (up to 32x) to search than the float32 index, while the rescoring is also extremely efficient.
This process is designed to be memory efficient and fast, with the binary index being small enough to fit in memory and the int8 index being loaded as a view. Additionally, the binary index is much faster (up to 32x) to search than the float32 index, while the rescoring is also extremely efficient.
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This space also showcases the tsdae-lemone-mbert-base, a sentence embedding model based on BERT fitted using Transformer-based Sequential Denoising Auto-Encoder for unsupervised sentence embedding learning with one objective : french legal domain adaptation.
This space also showcases the tsdae-lemone-mbert-base, a sentence embedding model based on BERT fitted using Transformer-based Sequential Denoising Auto-Encoder for unsupervised sentence embedding learning with one objective : french legal domain adaptation.
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Link to the 🤗 Space : 
Link to the 🤗 Space : 
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https://huggingface.co/spaces/louisbrulenaudet/legalkit-retrieval
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louisbrulenaudet/legalkit-retrieval
https://huggingface.co/spaces/louisbrulenaudet/legalkit-retrieval
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Notes:
Notes:
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The SentenceTransformer model currently in use is in beta and may not be suitable for direct use in production.
The SentenceTransformer model currently in use is in beta and may not be suitable for direct use in production.
LegalKit Retrieval, a binary Search with Scalar (int8) Rescoring through French legal codes is now available as a 🤗 Space.

This process is designed to be memory efficient and fast, with the binary index being small enough to fit in memory and the int8 index being loaded as a view. Additionally, the binary index is much faster (up to 32x) to search than the float32 index, while the rescoring is also extremely efficient.

This space also showcases the tsdae-lemone-mbert-base, a sentence embedding model based on BERT fitted using Transformer-based Sequential Denoising Auto-Encoder for unsupervised sentence embedding learning with one objective : french legal domain adaptation.

Link to the 🤗 Space : https://huggingface.co/spaces/louisbrulenaudet/legalkit-retrieval

Notes:
The SentenceTransformer model currently in use is in beta and may not be suitable for direct use in production.
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colin-r-carter marksmithh osanseviero tomaarsen samusenps ethanker
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Omar Sanseviero
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Tom Aarsen
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