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---
license: apache-2.0
dataset_info:
features:
- name: model
dtype: string
- name: query_prefix
dtype: string
- name: passage_prefix
dtype: string
- name: embedding_size
dtype: int64
- name: revision
dtype: string
- name: model_type
dtype: string
- name: torch_dtype
dtype: string
- name: max_length
dtype: int64
splits:
- name: train
num_bytes: 475
num_examples: 5
download_size: 4533
dataset_size: 475
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
task_categories:
- tabular-to-text
- tabular-classification
- sentence-similarity
- question-answering
language:
- en
tags:
- legal
- reference
- automation
- HFforLegal
pretty_name: Reference models for integration into HF for Legal
size_categories:
- n<1K
---
## Dataset Description
- **Repository:** https://huggingface.co/datasets/HFforLegal/embedding-models
- **Leaderboard:** N/A
- **Point of Contact:** [Louis Brulé Naudet](mailto:[email protected])
-
# Reference models for integration into HF for Legal 🤗
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:
<img src="https://huggingface.co/spaces/HFforLegal/README/resolve/main/assets/HF%20for%20Legal%20architecture%20for%20easy%20deployment.png">
## Citing & Authors
If you use this dataset in your research, please use the following BibTeX entry.
```BibTeX
@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}},
}
```
## Feedback
If you have any feedback, please reach out at [[email protected]](mailto:[email protected]). |