This dataset comprises a collection of models aimed at streamlining and partially automating the embedding process. Each model entry within this dataset includes essential information such as model identifiers, embedding configurations, and specific parameters, ensuring that users can seamlessly integrate these models into their workflows with minimal setup and maximum efficiency.

Dataset Structure

Field	Type	Description
`model`	str	The identifier of the model, typically formatted as `organization/model-name`.
`query_prefix`	str	A prefix string added to query inputs to delineate them.
`passage_prefix`	str	A prefix string added to passage inputs to delineate them.
`embedding_size`	int	The dimensional size of the embedding vectors produced by the model.
`revision`	str	The specific revision identifier of the model to ensure consistency.
`model_type`	str	The architectural type of the model, such as `xlm-roberta` or `qwen2`.
`torch_dtype`	str	The data type utilized in PyTorch operations, such as `float32`.
`max_length`	int	The maximum input length the model can process, specified in tokens.

Organization architecture

In order to simplify the deployment of the organization's various tools, we propose a simple architecture in which datasets containing the various legal and contractual texts are doubled by datasets containing embeddings for different models, to enable simplified index creation for Spaces initialization and the provision of vector data for the GPU-poor. A simplified representation might look like this:

Citing & Authors

If you use this dataset in your research, please use the following BibTeX entry.

@misc{HFforLegal2024,
  author =       {Louis Brulé Naudet},
  title =        {Reference models for integration into HF for Legal},
  year =         {2024}
  howpublished = {\url{https://huggingface.co/datasets/HFforLegal/embedding-models}},
}

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