Sociovestix
/

lenu_US-NY

@@ -20,203 +20,73 @@ model-index:
       name: f1 macro
       args:
         average: macro
 ---
-# Model Card for Model ID
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-#  Table of Contents
-1. [Model Details](#model-details)
-2. [Uses](#uses)
-3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
-4. [Training Details](#training-details)
-5. [Evaluation](#evaluation)
-6. [Model Examination](#model-examination-optional)
-7. [Environmental Impact](#environmental-impact)
-8. [Technical Specifications](#technical-specifications-optional)
-9. [Citation](#citation-optional)
-10. [Glossary](#glossary-optional)
-11. [More Information](#more-information-optional)
-12. [Model Card Authors](#model-card-authors-optional)
-13. [Model Card Contact](#model-card-contact)
-14. [How To Get Started With the Model](#how-to-get-started-with-the-model)
-# Model Details
 ## Model Description
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 # Uses
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-## Direct Use
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-## Downstream Use [optional]
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-## Out-of-Scope Use
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-# Bias, Risks, and Limitations
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-## Recommendations
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-# Training Details
-## Training Data
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-## Training Procedure [optional]
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-# Evaluation
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-## Testing Data, Factors & Metrics
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-### Factors
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-## Results
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-# Model Examination [optional]
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-# Environmental Impact
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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-# Technical Specifications [optional]
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-# Citation [optional]
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-# Glossary [optional]
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-# More Information [optional]
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-# Model Card Authors [optional]
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-# Model Card Contact
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-# How to Get Started with the Model
-Use the code below to get started with the model.
-<details>
-<summary> Click to expand </summary>
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-</details>

       name: f1 macro
       args:
         average: macro
+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"
 ---
+# LENU - Legal Entity Name Understanding for US New York
+A [finbert](https://huggingface.co/yiyanghkust/finbert-pretrain) 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]:** yiyanghkust/finbert-pretrain
+- **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.