Sociovestix
/

lenu_PT

@@ -24,197 +24,61 @@ model-index:
         average: macro
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         average: macro
 ---
+# LENU - Legal Entity Name Understanding for Portugal
+A [BERT multilingual](https://huggingface.co/google-bert/bert-base-multilingual-uncased) based model model fine-tuned on Portuguese legal entity names (jurisdiction PT) 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 Portuguese legal entities and ELF Codes within the Jurisdiction "PT".
+- **Developed by:** [GLEIF](https://gleif.org) and [Sociovestix Labs](https://huggingface.co/Sociovestix)
+- **License:** Creative Commons (CC0) license
+- **Finetuned from model [optional]:** bert-base-multilingual-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.