
# LENU - Legal Entity Name Understanding for Czech Republic

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

- **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": "ALU \u2013 SV CZ, s.r.o."}, {"text": "CZ STAVEBN\u00cd HOLDING, a.s."}, {"text": "NOVA Real Estate - podfond 1"}, {"text": "M\u011bsto Libu\u0161\u00edn"}, {"text": "Ing. Jan \u0158ezn\u00edk"}, {"text": "Stavebn\u00ed bytov\u00e9 dru\u017estvo Jind\u0159ich\u016fv Hradec"}, {"text": "SHERLOG SE"}, {"text": "Interim Investment sv\u011b\u0159ensk\u00fd fond"}, {"text": "Arcidiec\u00e9zn\u00ed charita Praha"}, {"text": "Nadace Kardiocentrum \u010cesk\u00e9 Bud\u011bjovice"}, {"text": "Svaz model\u00e1\u0159\u016f \u010cesk\u00e9 republiky z.s."}, {"text": "VAN GRAAF, k.s."}, {"text": "Odbory KOVO MB"}, {"text": "Vilau v.o.s."}, {"text": "Spole\u010denstv\u00ed vlastn\u00edk\u016f jednotek Purky\u0148ova 1106/17, Opava"}, {"text": "OSMA - \u010cR - OJ034"}, {"text": "\u0158\u00edmskokatolick\u00e1 farnost T\u011bchlovice"}, {"text": "Nada\u010dn\u00ed fond TELEPACE"}, {"text": "MILNEA st\u00e1tn\u00ed podnik v likvidaci"}, {"text": "\u010cIPA, o.p.s."}, {"text": "\u00dastav pro evropskou integraci z.\u00fa."}, {"text": "AWP P&C \u010cesk\u00e1 republika - od\u0161t\u011bpn\u00fd z\u00e1vod zahrani\u010dn\u00ed pr\u00e1vnick\u00e9 osoby"}, {"text": "Moravskoslezsk\u00fd kraj"}, {"text": "ecoenerg Windkraft GmbH & Co. KG, organiza\u010dn\u00ed slo\u017eka"}, {"text": "\u00dastav experiment\u00e1ln\u00ed botaniky AV \u010cR, v. v. i."}, {"text": "NIX.CZ, z.s.p.o."}, {"text": "Obvodn\u00ed soud pro Prahu 9"}, {"text": "Svazek obc\u00ed pro vodovody a kanalizace \u0160lapanicko"}, {"text": "Intel Czech Tradings, Inc., organiza\u010dn\u00ed slo\u017eka"}], "model-index": [{"name": "Sociovestix/lenu_CZ", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "lenu", "type": "Sociovestix/lenu", "config": "CZ", "split": "test", "revision": "fbe0b4b5b8d6950c10f5710f2c987728635a4afe"}, "metrics": [{"type": "f1", "value": 0.9935219911353563, "name": "f1"}, {"type": "f1", "value": 0.7750705528144344, "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 Czech Republic

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

- 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 Czech Republic

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

- 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 Czech Republic

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

- 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 Czech Republic

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

- 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.