---
widget:
- text: 949-959 SOUTHERN BLVD LLC
- text: Lumico Life Insurance Company of New York
- text: BNP PARIBAS ASSET MANAGEMENT USA HOLDINGS, INC.
- text: QB ASSOCIATES, L.P.
- text: THE LOTOS CLUB
- text: LOMBARD & GELIBETER LLP
- text: Walden Savings Bank
- text: Adirondack Bank
- text: The North Country Savings Bank
model-index:
- name: Sociovestix/lenu_US-NY
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: lenu
      type: Sociovestix/lenu
      config: US-NY
      split: test
      revision: f4d57b8d77a49ec5c62d899c9a213d23cd9f9428
    metrics:
    - type: f1
      value: 0.9661246612466124
      name: f1
    - type: f1
      value: 0.8012750940701334
      name: f1 macro
      args:
        average: macro
---

# LENU - Legal Entity Name Understanding for US New York


A [BERT](https://huggingface.co/google-bert/bert-base-uncased) based model fine-tuned on US New York legal entity names (jurisdiction US-NY) from the Global [Legal Entity Identifier](https://www.gleif.org/en/about-lei/introducing-the-legal-entity-identifier-lei)
(LEI) System with the goal to detect [Entity Legal Form (ELF) Codes](https://www.gleif.org/en/about-lei/code-lists/iso-20275-entity-legal-forms-code-list).

---------------

<h1 align="center">
<a href="https://gleif.org">
<img src="http://sdglabs.ai/wp-content/uploads/2022/07/gleif-logo-new.png" width="220px" style="display: inherit">
</a>
</h1><br>
<h3 align="center">in collaboration with</h3> 
<h1 align="center">
<a href="https://sociovestix.com">
<img src="https://sociovestix.com/img/svl_logo_centered.svg" width="700px" style="width: 100%">
</a>
</h1><br>

---------------

## Model Description

<!-- Provide a longer summary of what this model is. -->

The model has been created as part of a collaboration of the [Global Legal Entity Identifier Foundation](https://gleif.org) (GLEIF) and
[Sociovestix Labs](https://sociovestix.com) with the goal to explore how Machine Learning can support in detecting the ELF Code solely based on an entity's legal name and legal jurisdiction.
See also the open source python library [lenu](https://github.com/Sociovestix/lenu), which supports in this task.

The model has been trained on the dataset [lenu](https://huggingface.co/datasets/Sociovestix), with a focus on US New York legal entities and ELF Codes within the Jurisdiction "US-NY".

- **Developed by:** [GLEIF](https://gleif.org) and [Sociovestix Labs](https://huggingface.co/Sociovestix)
- **License:** Creative Commons (CC0) license
- **Finetuned from model [optional]:** bert-base-uncased
- **Resources for more information:** [Press Release](https://www.gleif.org/en/newsroom/press-releases/machine-learning-new-open-source-tool-developed-by-gleif-and-sociovestix-labs-enables-organizations-everywhere-to-automatically-)

# Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

An entity's legal form is a crucial component when verifying and screening organizational identity.
The wide variety of entity legal forms that exist within and between jurisdictions, however, has made it difficult for large organizations to capture legal form as structured data.
The Jurisdiction specific models of [lenu](https://github.com/Sociovestix/lenu), trained on entities from
GLEIF’s Legal Entity Identifier (LEI) database of over two million records, will allow banks, 
investment firms, corporations, governments, and other large organizations to retrospectively analyze
their master data, extract the legal form from the unstructured text of the legal name and
uniformly apply an ELF code to each entity type, according to the ISO 20275 standard.


# Licensing Information

This model, which is trained on LEI data, is available under Creative Commons (CC0) license. 
See [gleif.org/en/about/open-data](https://gleif.org/en/about/open-data).

# Recommendations

Users should always consider the score of the suggested ELF Codes. For low score values it may be necessary to manually review the affected entities.