
# LENU - Legal Entity Name Understanding for Hungary

A Bert (multilingual uncased) model fine-tuned on hungarian legal entity names (jurisdiction HU) 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 hungarian legal entities and ELF Codes within the Jurisdiction "HU".

- **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.
{"widget": [{"text": "NL-Consult Tan\u00e1csad\u00f3 Korl\u00e1tolt Felel\u0151ss\u00e9g\u0171 T\u00e1rsas\u00e1g"}, {"text": "D-Tag Analytics Z\u00e1rtk\u00f6r\u0171en M\u0171k\u00f6d\u0151 R\u00e9szv\u00e9nyt\u00e1rsas\u00e1g"}, {"text": "Felis Mag\u00e1nt\u0151kealap"}, {"text": "MMV Magyar Mag\u00e1nvas\u00fat Z\u00e1rtk\u00f6r\u0171en M\u0171k\u00f6d\u0151 R\u00e9szv\u00e9nyt\u00e1rsas\u00e1g"}, {"text": "MEDIKER Bt"}, {"text": "Keller Tibor Bizalmi Vagyonkezel\u0151 \u00e1ltal kezelt vagyon"}, {"text": "Kreat\u00edv B\u00e1zis Alap\u00edtv\u00e1ny"}, {"text": "\"METEOR\" Term\u00e9szetbar\u00e1tok Turista Egyes\u00fclete"}, {"text": "DR. VINCZE GERGELY \u00dcGYV\u00c9DI IRODA"}, {"text": "Vonyarcvashegy Nagyk\u00f6zs\u00e9g \u00d6nkorm\u00e1nyzata"}, {"text": "Gryffindor Trust"}, {"text": "PannErgy Nyilv\u00e1nosan M\u0171k\u00f6d\u0151 R\u00e9szv\u00e9nyt\u00e1rsas\u00e1g"}, {"text": "Bizalmi vagyonkezel\u00e9ssel kezelt vagyon Wessely Vilmos"}, {"text": "Taurus Orsz\u00e1gos \u00d6nk\u00e9ntes K\u00f6lcs\u00f6n\u00f6s Nyugd\u00edjp\u00e9nzt\u00e1r"}, {"text": "Dunamell\u00e9ki Reform\u00e1tus Egyh\u00e1zker\u00fclet R\u00e1day Gy\u0171jtem\u00e9nye"}, {"text": "Nyugd\u00edj-kieg\u00e9sz\u00edt\u00e9s K\u00f6z\u00e9rdek\u0171 Nyugd\u00edjas Sz\u00f6vetkezet"}, {"text": "ALTEO Munkav\u00e1llal\u00f3i R\u00e9sztulajdonosi Program Szervezete"}, {"text": "Sz\u00e9chenyi \u00fati 92 lak\u00e1sos OTP T\u00e1rsash\u00e1z"}, {"text": "Honv\u00e9delmi Miniszt\u00e9rium"}, {"text": "Eventus \u00dczleti, M\u0171v\u00e9szeti Szakgimn\u00e1zium, Technikum, Gimn\u00e1zium, Szakk\u00e9pz\u0151 Iskola, Alapfok\u00fa M\u0171v\u00e9szeti Iskola \u00e9s Koll\u00e9gium"}, {"text": "VDSZ Gy\u00f3gyszergy\u00e1ri Szakszervezete"}, {"text": "Magyar Agr\u00e1r-, \u00c9lelmiszergazdas\u00e1gi \u00e9s Vid\u00e9kfejleszt\u00e9si Kamara"}, {"text": "Budapest Orsz\u00e1gos K\u00f6telez\u0151 Mag\u00e1nnyugd\u00edjp\u00e9nzt\u00e1r"}, {"text": "Dr. Orell Zsolt V\u00e9grehajt\u00f3i Irod\u00e1ja"}, {"text": "CEU Ingatlankezel\u0151 SE Z\u00e1rtk\u00f6r\u0171en M\u0171k\u00f6d\u0151 Eur\u00f3pai R\u00e9szv\u00e9nyt\u00e1rsas\u00e1g"}, {"text": "MIX Erd\u0151birtokoss\u00e1gi T\u00e1rsulat"}, {"text": "KARO\" Ingatlanhasznos\u00edt\u00f3 \u00e9s \u00dczemeltet\u0151 K\u00f6zkereseti T\u00e1rsas\u00e1g"}, {"text": "GEA Food Solutions International B.V. Convenience Food Systems International Magyarorsz\u00e1g Fi\u00f3ktelep"}], "model-index": [{"name": "Sociovestix/lenu_HU", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "lenu", "type": "Sociovestix/lenu", "config": "HU", "split": "test", "revision": "fbe0b4b5b8d6950c10f5710f2c987728635a4afe"}, "metrics": [{"type": "f1", "value": 0.9401789919924635, "name": "f1"}, {"type": "f1", "value": 0.7201515122099558, "name": "f1 macro", "args": {"average": "macro"}}]}]}]}
transformers pytorch bert text-classification model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #model-index #autotrain_compatible #endpoints_compatible #region-us 


# LENU - Legal Entity Name Understanding for Hungary

A Bert (multilingual uncased) model fine-tuned on hungarian legal entity names (jurisdiction HU) from the Global Legal Entity Identifier
(LEI) System with the goal to detect Entity Legal Form (ELF) Codes.

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

<h1 align="center">
<a href="URL">
<img src="URL width="220px" style="display: inherit">
</a>
</h1><br>
<h3 align="center">in collaboration with</h3> 
<h1 align="center">
<a href="URL">
<img src="URL width="700px" style="width: 100%">
</a>
</h1><br>

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

## Model Description



The model has been created as part of a collaboration of the Global Legal Entity Identifier Foundation (GLEIF) and
Sociovestix Labs 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, which supports in this task.

The model has been trained on the dataset lenu, with a focus on hungarian legal entities and ELF Codes within the Jurisdiction "HU".

- Developed by: GLEIF and Sociovestix Labs
- License: Creative Commons (CC0) license
- Finetuned from model [optional]: bert-base-multilingual-uncased
- Resources for more information: Press Release

# Uses



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, 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 URL

# 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.
# LENU - Legal Entity Name Understanding for Hungary

A Bert (multilingual uncased) model fine-tuned on hungarian legal entity names (jurisdiction HU) from the Global Legal Entity Identifier
(LEI) System with the goal to detect Entity Legal Form (ELF) Codes.

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

<h1 align="center">
<a href="URL">
<img src="URL width="220px" style="display: inherit">
</a>
</h1><br>
<h3 align="center">in collaboration with</h3> 
<h1 align="center">
<a href="URL">
<img src="URL width="700px" style="width: 100%">
</a>
</h1><br>

--------------- ## Model Description



The model has been created as part of a collaboration of the Global Legal Entity Identifier Foundation (GLEIF) and
Sociovestix Labs 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, which supports in this task.

The model has been trained on the dataset lenu, with a focus on hungarian legal entities and ELF Codes within the Jurisdiction "HU".

- Developed by: GLEIF and Sociovestix Labs
- License: Creative Commons (CC0) license
- Finetuned from model [optional]: bert-base-multilingual-uncased
- Resources for more information: Press Release # Uses



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, 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 URL # 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.
TAGS
#transformers #pytorch #bert #text-classification #model-index #autotrain_compatible #endpoints_compatible #region-us 
 # LENU - Legal Entity Name Understanding for Hungary

A Bert (multilingual uncased) model fine-tuned on hungarian legal entity names (jurisdiction HU) from the Global Legal Entity Identifier
(LEI) System with the goal to detect Entity Legal Form (ELF) Codes.

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

<h1 align="center">
<a href="URL">
<img src="URL width="220px" style="display: inherit">
</a>
</h1><br>
<h3 align="center">in collaboration with</h3> 
<h1 align="center">
<a href="URL">
<img src="URL width="700px" style="width: 100%">
</a>
</h1><br>

--------------- ## Model Description



The model has been created as part of a collaboration of the Global Legal Entity Identifier Foundation (GLEIF) and
Sociovestix Labs 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, which supports in this task.

The model has been trained on the dataset lenu, with a focus on hungarian legal entities and ELF Codes within the Jurisdiction "HU".

- Developed by: GLEIF and Sociovestix Labs
- License: Creative Commons (CC0) license
- Finetuned from model [optional]: bert-base-multilingual-uncased
- Resources for more information: Press Release # Uses



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, 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 URL # 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.
TAGS
#transformers #pytorch #bert #text-classification #model-index #autotrain_compatible #endpoints_compatible #region-us 
# LENU - Legal Entity Name Understanding for Hungary

A Bert (multilingual uncased) model fine-tuned on hungarian legal entity names (jurisdiction HU) from the Global Legal Entity Identifier
(LEI) System with the goal to detect Entity Legal Form (ELF) Codes.

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

<h1 align="center">
<a href="URL">
<img src="URL width="220px" style="display: inherit">
</a>
</h1><br>
<h3 align="center">in collaboration with</h3> 
<h1 align="center">
<a href="URL">
<img src="URL width="700px" style="width: 100%">
</a>
</h1><br>

---------------## Model Description



The model has been created as part of a collaboration of the Global Legal Entity Identifier Foundation (GLEIF) and
Sociovestix Labs 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, which supports in this task.

The model has been trained on the dataset lenu, with a focus on hungarian legal entities and ELF Codes within the Jurisdiction "HU".

- Developed by: GLEIF and Sociovestix Labs
- License: Creative Commons (CC0) license
- Finetuned from model [optional]: bert-base-multilingual-uncased
- Resources for more information: Press Release# Uses



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, 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 URL# 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.