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My biggest release of the year: a series of 7 specialized embedding models for information retrieval within tax documents, is now available for free on Hugging Face 🤗
My biggest release of the year: a series of 7 specialized embedding models for information retrieval within tax documents, is now available for free on Hugging Face 🤗
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These new models aim to offer an open source alternative for in-domain semantic search from large text corpora and will improve RAG systems and context addition for large language models.
These new models aim to offer an open source alternative for in-domain semantic search from large text corpora and will improve RAG systems and context addition for large language models.
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Trained on more than 43 million tax tokens derived from semi-synthetic and raw-synthetic data, enriched by various methods (in particular MSFT's evol-instruct by 
Trained on more than 43 million tax tokens derived from semi-synthetic and raw-synthetic data, enriched by various methods (in particular MSFT's evol-instruct by 
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@intfloat
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), and corrected by humans, this project is the fruit of hundreds of hours of work and is the culmination of a global effort to open up legal technologies that has only just begun.
), and corrected by humans, this project is the fruit of hundreds of hours of work and is the culmination of a global effort to open up legal technologies that has only just begun.
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A big thank you to Microsoft for Startups for giving me access to state-of-the-art infrastructure to train these models, and to 
A big thank you to Microsoft for Startups for giving me access to state-of-the-art infrastructure to train these models, and to 
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@julien-c
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, 
, 
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@clem
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  🤗, 
  🤗, 
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@thomwolf
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 and the whole HF team for the inference endpoint API and the generous provision of Meta LLama-3.1-70B. Special thanks also to 
 and the whole HF team for the inference endpoint API and the generous provision of Meta LLama-3.1-70B. Special thanks also to 
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@tomaarsen
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 for his invaluable advice on training embedding models and Loss functions ❤️
 for his invaluable advice on training embedding models and Loss functions ❤️
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Models are available on my personal HF page, into the Lemone-embed collection: 
Models are available on my personal HF page, into the Lemone-embed collection: 
resource
https://huggingface.co/collections/louisbrulenaudet/lemone-embed-66fdc24000df732b395df29b
collection
louisbrulenaudet/lemone-embed-66fdc24000df732b395df29b
https://huggingface.co/collections/louisbrulenaudet/lemone-embed-66fdc24000df732b395df29b
My biggest release of the year: a series of 7 specialized embedding models for information retrieval within tax documents, is now available for free on Hugging Face 🤗

These new models aim to offer an open source alternative for in-domain semantic search from large text corpora and will improve RAG systems and context addition for large language models.

Trained on more than 43 million tax tokens derived from semi-synthetic and raw-synthetic data, enriched by various methods (in particular MSFT's evol-instruct by @intfloat), and corrected by humans, this project is the fruit of hundreds of hours of work and is the culmination of a global effort to open up legal technologies that has only just begun.

A big thank you to Microsoft for Startups for giving me access to state-of-the-art infrastructure to train these models, and to @julien-c, @clem  🤗, @thomwolf and the whole HF team for the inference endpoint API and the generous provision of Meta LLama-3.1-70B. Special thanks also to @tomaarsen for his invaluable advice on training embedding models and Loss functions ❤️

Models are available on my personal HF page, into the Lemone-embed collection: https://huggingface.co/collections/louisbrulenaudet/lemone-embed-66fdc24000df732b395df29b
❤️
tomaarsen clem yashisfine sensei-ml John6666 a9i Tonic julien-c
👍
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yash negi
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