added scripts and readme

.gitignore

@@ -0,0 +1,4 @@
+
+__pycache__
+poetry.lock
+pyproject.toml

README.md

@@ -1,3 +1,1271 @@
 ---
-license: cc0-1.0
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+    dtype: string
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+- config_name: DE
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+  - name: Entity.LegalForm.EntityLegalFormCode
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+          '18': GNXT
+          '19': UNJ2
+  splits:
+  - name: train
+    num_bytes: 3706306
+    num_examples: 62848
+  - name: validation
+    num_bytes: 530621
+    num_examples: 8979
+  - name: test
+    num_bytes: 1060390
+    num_examples: 17957
+  download_size: 300333753
+  dataset_size: 5297317
+- config_name: 'NO'
+  features:
+  - name: LEI
+    dtype: string
+  - name: Entity.LegalName
+    dtype: string
+  - name: Entity.LegalForm.EntityLegalFormCode
+    dtype:
+      class_label:
+        names:
+          '0': YI42
+          '1': LJJW
+          '2': V06W
+          '3': '8888'
+          '4': IQGE
+          '5': 3C7U
+          '6': FSBD
+          '7': EXD7
+          '8': K5P8
+          '9': 8S9H
+          '10': GYY6
+          '11': 4ZRR
+          '12': 3L58
+          '13': R71C
+          '14': BJ65
+          '15': M9IQ
+          '16': O0EU
+          '17': CF5L
+          '18': 326Y
+          '19': ZQ0Q
+          '20': Q0Q1
+          '21': PB3V
+          '22': 9DI1
+          '23': AEV1
+          '24': YTMC
+          '25': 5ZTZ
+          '26': 50TD
+  splits:
+  - name: train
+    num_bytes: 1223064
+    num_examples: 23277
+  - name: validation
+    num_bytes: 174418
+    num_examples: 3326
+  - name: test
+    num_bytes: 349905
+    num_examples: 6651
+  download_size: 300333753
+  dataset_size: 1747387
+- config_name: PL
+  features:
+  - name: LEI
+    dtype: string
+  - name: Entity.LegalName
+    dtype: string
+  - name: Entity.LegalForm.EntityLegalFormCode
+    dtype:
+      class_label:
+        names:
+          '0': FJ0E
+          '1': O7XB
+          '2': RBHP
+          '3': BSJT
+          '4': ZVVM
+          '5': '8888'
+          '6': OMX0
+          '7': 629I
+          '8': KM66
+          '9': H7OD
+          '10': 8TOF
+          '11': WUJ2
+          '12': T7PB
+          '13': 96XK
+          '14': ZZKE
+          '15': 13ZV
+          '16': LT9U
+          '17': 3BJG
+          '18': SVA3
+          '19': SP4S
+          '20': AL9T
+          '21': B21W
+          '22': 60BG
+          '23': RUCO
+          '24': JCKO
+          '25': J3A3
+          '26': WNX1
+          '27': QUX1
+          '28': FQ5Y
+          '29': 5F76
+          '30': WOK7
+          '31': QYL4
+          '32': GZE5
+          '33': SMIS
+          '34': CY1M
+          '35': YLZL
+  splits:
+  - name: train
+    num_bytes: 1164275
+    num_examples: 14167
+  - name: validation
+    num_bytes: 168331
+    num_examples: 2024
+  - name: test
+    num_bytes: 331549
+    num_examples: 4048
+  download_size: 300333753
+  dataset_size: 1664155
+- config_name: SE
+  features:
+  - name: LEI
+    dtype: string
+  - name: Entity.LegalName
+    dtype: string
+  - name: Entity.LegalForm.EntityLegalFormCode
+    dtype:
+      class_label:
+        names:
+          '0': XJHM
+          '1': CX05
+          '2': '8888'
+          '3': BEAY
+          '4': BYQJ
+          '5': 1TN0
+          '6': OJ9I
+          '7': C61P
+          '8': 2UAX
+          '9': AZTO
+          '10': O1QI
+          '11': SSOM
+          '12': G04R
+          '13': M0Y0
+          '14': '9999'
+          '15': WZDB
+          '16': PDQ0
+  splits:
+  - name: train
+    num_bytes: 1978495
+    num_examples: 33687
+  - name: validation
+    num_bytes: 282253
+    num_examples: 4813
+  - name: test
+    num_bytes: 566233
+    num_examples: 9625
+  download_size: 300333753
+  dataset_size: 2826981
+- config_name: US-CA
+  features:
+  - name: LEI
+    dtype: string
+  - name: Entity.LegalName
+    dtype: string
+  - name: Entity.LegalForm.EntityLegalFormCode
+    dtype:
+      class_label:
+        names:
+          '0': '8888'
+          '1': 5HQ4
+          '2': H1UM
+          '3': EI4J
+          '4': K7YU
+          '5': SQ7B
+          '6': PZR6
+          '7': 7CDL
+          '8': G1P6
+          '9': CVXK
+          '10': KQXA
+          '11': 4JCS
+          '12': BADE
+          '13': '9999'
+  splits:
+  - name: train
+    num_bytes: 275962
+    num_examples: 4315
+  - name: validation
+    num_bytes: 39591
+    num_examples: 617
+  - name: test
+    num_bytes: 79126
+    num_examples: 1233
+  download_size: 300333753
+  dataset_size: 394679
+- config_name: US-NY
+  features:
+  - name: LEI
+    dtype: string
+  - name: Entity.LegalName
+    dtype: string
+  - name: Entity.LegalForm.EntityLegalFormCode
+    dtype:
+      class_label:
+        names:
+          '0': '8888'
+          '1': 51RC
+          '2': PJ10
+          '3': SDX0
+          '4': XIZI
+          '5': BO6L
+          '6': 4VH5
+          '7': '9999'
+          '8': M0ER
+          '9': EPCY
+  splits:
+  - name: train
+    num_bytes: 211229
+    num_examples: 3331
+  - name: validation
+    num_bytes: 30484
+    num_examples: 476
+  - name: test
+    num_bytes: 60357
+    num_examples: 952
+  download_size: 300333753
+  dataset_size: 302070
+- config_name: VG
+  features:
+  - name: LEI
+    dtype: string
+  - name: Entity.LegalName
+    dtype: string
+  - name: Entity.LegalForm.EntityLegalFormCode
+    dtype:
+      class_label:
+        names:
+          '0': 6EH6
+          '1': '8888'
+          '2': YOP9
+          '3': '9999'
+          '4': Q62B
+          '5': ZHED
+          '6': GLCI
+          '7': N28C
+          '8': BST2
+          '9': JS65
+  splits:
+  - name: train
+    num_bytes: 649068
+    num_examples: 10666
+  - name: validation
+    num_bytes: 92764
+    num_examples: 1524
+  - name: test
+    num_bytes: 185500
+    num_examples: 3048
+  download_size: 300333753
+  dataset_size: 927332
+- config_name: CN
+  features:
+  - name: LEI
+    dtype: string
+  - name: Entity.LegalName
+    dtype: string
+  - name: Entity.LegalForm.EntityLegalFormCode
+    dtype:
+      class_label:
+        names:
+          '0': ECAK
+          '1': '8888'
+          '2': DLEK
+          '3': 1IWK
+          '4': B5UZ
+          '5': I39S
+          '6': SH05
+          '7': CYV6
+          '8': 2M6Y
+          '9': BDTI
+          '10': V816
+          '11': YXJ5
+          '12': GGZ5
+          '13': 8XAW
+          '14': RV48
+          '15': UMCR
+          '16': ESZ8
+          '17': 6NSC
+          '18': E4FG
+          '19': IBEM
+  splits:
+  - name: train
+    num_bytes: 2485091
+    num_examples: 33898
+  - name: validation
+    num_bytes: 354362
+    num_examples: 4843
+  - name: test
+    num_bytes: 709914
+    num_examples: 9686
+  download_size: 300333753
+  dataset_size: 3549367
+- config_name: ZA
+  features:
+  - name: LEI
+    dtype: string
+  - name: Entity.LegalName
+    dtype: string
+  - name: Entity.LegalForm.EntityLegalFormCode
+    dtype:
+      class_label:
+        names:
+          '0': GQVQ
+          '1': '8888'
+          '2': XE4Z
+          '3': R59V
+          '4': 4YUU
+          '5': U89P
+          '6': R155
+          '7': J7L0
+  splits:
+  - name: train
+    num_bytes: 52499
+    num_examples: 795
+  - name: validation
+    num_bytes: 7541
+    num_examples: 114
+  - name: test
+    num_bytes: 15198
+    num_examples: 228
+  download_size: 300333753
+  dataset_size: 75238
 ---
+
+# Dataset Card for "lenu - Legal Entity Name Understanding"
+
+---------------
+
+<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>
+
+---------------
+
+## Table of Contents
+- [Dataset Card Creation Guide](#dataset-card-creation-guide)
+  - [Table of Contents](#table-of-contents)
+  - [Dataset Description](#dataset-description)
+    - [Dataset Summary](#dataset-summary)
+    - [Languages](#languages)
+  - [Dataset Structure](#dataset-structure)
+    - [Data Instances](#data-instances)
+    - [Data Fields](#data-fields)
+    - [Data Splits](#data-splits)
+- [Licensing Information](#licensing-information)
+
+## Dataset Description
+
+- **Homepage:** [gleif.org](https://gleif.org)
+- **Repository:** [The LENU project](https://github.com/Sociovestix/lenu)
+- **Point of Contact:** [aarimond](https://huggingface.co/aarimond)
+
+### Dataset Summary
+
+This dataset contains legal entity names from the Global LEI System in which each entity is
+assigned with a unique
+[Legal Entity Identifier](https://www.gleif.org/en/about-lei/introducing-the-legal-entity-identifier-lei)
+(LEI) code (ISO Standard 17441)
+along with their corresponding
+[Entity Legal Form (ELF) Codes](https://www.gleif.org/en/about-lei/code-lists/iso-20275-entity-legal-forms-code-list)
+(ISO Standard 20275),
+which specifies the legal form of each entity.
+
+The dataset 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 data is created from LEI data downloaded from 
+[GLEIF's public website](https://www.gleif.org/en/lei-data/gleif-golden-copy/download-the-golden-copy/)
+(Date: 2022-11-01 00:00), where it is accessible free of charge.
+It is divided into subsets for a selection of legal jurisdictions, whereas each Jurisdiction has its own set of ELF Codes.
+The ELF Code reference list can be downloaded [here](https://www.gleif.org/en/about-lei/code-lists/iso-20275-entity-legal-forms-code-list).
+
+
+### Languages
+
+The data contains several major Jurisdictions (e.g. US-DE (US Delaware), JP (Japan), DE (Germany) and others).
+Legal entity names usually follow certain language patterns, depending on which jurisdiction they are located in.
+Thus, we apply models that are pre-trained on the corresponding language.
+
+
+## Dataset Structure
+
+### Data Instances
+
+The data contains of the LEI, the corresponding legal name and ELF Code. 
+
+```
+{
+  'LEI': '254900OMZ079O2SDWA75',
+  'Entity.LegalName': 'Park Reseda Mortgage LLC',
+  'Entity.LegalForm.EntityLegalFormCode': 0 
+}
+```
+
+### Data Fields
+
+This is just a subset of available fields in the LEI system. All fields are described in detail in GLEIF's 
+[LEI Common Data Format (CDF)](https://www.gleif.org/en/about-lei/common-data-file-format/current-versions/level-1-data-lei-cdf-3-1-format).
+
+- `LEI`: The [Legal Entity Identifier](https://www.gleif.org/en/about-lei/introducing-the-legal-entity-identifier-lei) Code. Uniquely identifies a Legal Entity.
+- `Entity.LegalName`: The official name of the legal entity as registered in the LEI system.
+- `Entity.LegalForm.EntityLegalFormCode`: class encoded column which contains the [Entity Legal Form Code](https://www.gleif.org/en/about-lei/code-lists/iso-20275-entity-legal-forms-code-list)
+
+
+### Data Splits
+
+We have divided each jurisdiction's subset into stratified train (70%), validation (10%) and test (20%) splits.
+ELF Codes that appear less than three times in a Jurisdiction have been removed. 
+
+
+## Licensing Information
+
+This dataset, which is based on LEI data, is available under Creative Commons (CC0) license. 
+See [gleif.org/en/about/open-data](https://gleif.org/en/about/open-data).

_classnames.py

@@ -0,0 +1,87 @@
+# `lenu - Legal Entity Name Understanding` by GLEIF and Sociovestix Labs
+# Written in 2022 by Sociovestix Labs
+# To the extent possible under law, the author(s) have dedicated all copyright
+# and related and neighboring rights to this software to the public domain
+# worldwide. This software is distributed without any warranty.
+#
+# You should have received a copy of the CC0 Public Domain Dedication along
+# with this software.
+# If not, see <http://creativecommons.org/publicdomain/zero/1.0/>.
+
+
+"""This helper script creates the classnames variable for lenu.py"""
+
+
+from lenu import URL
+import pandas
+
+
+relevant_cols = [
+    "LEI",
+    "Entity.LegalName",
+    "Entity.LegalForm.EntityLegalFormCode",
+    "Entity.LegalJurisdiction",
+    "Entity.EntityCategory",
+    "Entity.EntityStatus",
+    "Registration.RegistrationStatus",
+]
+
+
+COL_LEI, COL_NAME, COL_ELF, COL_JUR, COL_CAT, COL_ESTATUS, COL_RSTATUS = relevant_cols
+
+
+if __name__ == "__main__":
+    d = pandas.read_csv(
+        URL,
+        compression="zip",
+        low_memory=True,
+        dtype=str,
+        # the following will prevent pandas from converting words like 'NA' to NaN. We want to work with the LEI data as is.
+        na_values=[""],
+        keep_default_na=False,
+        usecols=relevant_cols,
+    )
+
+    d_issued = d[(d[COL_ESTATUS] == "ACTIVE") & (d[COL_RSTATUS] == "ISSUED")]
+
+    classnames = {
+        jur: classes
+        for jur, classes in d_issued.groupby(COL_JUR)[COL_ELF]
+        .unique()
+        .apply(list)
+        .to_dict()
+        .items()
+        if jur
+        in [
+            "AT",
+            "AU",
+            "CH",
+            "CN",
+            "CZ",
+            "DE",
+            "DK",
+            "EE",
+            "ES",
+            "FI",
+            "GB",
+            "HU",
+            "IE",
+            "JP",
+            "KY",
+            "LU",
+            "NL",
+            "NO",
+            "PL",
+            "SE",
+            "US-CA",
+            "US-DE",
+            "US-NY",
+            "VG",
+            "LI",
+            "ZA",
+        ]  # not CA, BE, FR, BG
+    }
+
+    print("Please copy the following snippet into lenu.py:")
+    print("")
+    print("classnames = ", classnames)

lenu.py

@@ -0,0 +1,852 @@
+# coding=utf-8
+# Copyright 2020 The HuggingFace Datasets Authors and the current
+# dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# NOTICE:
+# this script is derivate work of
+# https://github.com/huggingface/datasets/blob/main/templates/new_dataset_script.py
+
+"""lenu - Legal Entity Name Understanding"""
+
+
+from io import BytesIO
+import os
+
+import datasets
+from datasets.builder import logging
+import fsspec
+import pandas
+from sklearn.model_selection import train_test_split
+
+
+_DESCRIPTION = """\
+    This dataset contains legal entity names from the Global LEI System in
+    which each entity is assigned with a unique Legal Entity Identifier (LEI)
+    code (ISO Standard 17441) along with their corresponding Entity Legal
+    Form (ELF) Codes (ISO Standard 20275) which specifies the legal form of
+    each entity.
+"""
+_HOMEPAGE = "gleif.org"
+_LICENSE = "cc0-1.0"
+
+URL = (
+    "https://goldencopy.gleif.org/api/v2/golden-copies/publishes/lei2/20221101-0000.csv"
+)
+
+
+# created through _classnames.py script
+classnames = {
+    "AT": [
+        "AXSB",
+        "EQOV",
+        "8888",
+        "ONF1",
+        "JTAV",
+        "DX6Z",
+        "ECWU",
+        "5WWO",
+        "1NOX",
+        "E9OX",
+        "AAL7",
+        "JJYT",
+        "UI81",
+        "GVPD",
+        "NIJH",
+        "8XDW",
+        "CAQ1",
+        "JQOI",
+        "O65B",
+        "G3R6",
+        "69H1",
+    ],
+    "AU": [
+        "TXVC",
+        "ADXG",
+        "R4KK",
+        "8888",
+        "7TPC",
+        "LZFR",
+        "Q82Q",
+        "BC38",
+        "XHCV",
+        "PQHL",
+        "J4JC",
+        "6W6X",
+        "9999",
+    ],
+    "CH": [
+        "MVII",
+        "8888",
+        "7MNN",
+        "FJG4",
+        "2JZ4",
+        "54WI",
+        "3EKS",
+        "FLNB",
+        "XJOT",
+        "H781",
+        "QSI2",
+        "DP2E",
+        "E0NE",
+        "5BEZ",
+        "AZA0",
+        "2B81",
+        "M848",
+        "1BL5",
+        "HX77",
+        "CQMY",
+        "9999",
+        "MRSY",
+        "GP8M",
+        "FFTN",
+        "L5DU",
+        "TL87",
+        "2XJA",
+        "W6A7",
+        "BF9N",
+    ],
+    "CN": [
+        "ECAK",
+        "8888",
+        "DLEK",
+        "1IWK",
+        "B5UZ",
+        "I39S",
+        "SH05",
+        "CYV6",
+        "2M6Y",
+        "BDTI",
+        "V816",
+        "YXJ5",
+        "GGZ5",
+        "8XAW",
+        "RV48",
+        "UMCR",
+        "ESZ8",
+        "6NSC",
+        "E4FG",
+        "IBEM",
+    ],
+    "CZ": [
+        "9HLU",
+        "6CQN",
+        "TNBA",
+        "9RVC",
+        "ZQO8",
+        "RHFQ",
+        "747U",
+        "6D9L",
+        "3G3D",
+        "95G8",
+        "SNWJ",
+        "J8PB",
+        "JCAD",
+        "CATU",
+        "CD28",
+        "IQ9O",
+        "HY6K",
+        "UFDA",
+        "QIEL",
+        "7OZQ",
+        "6FAI",
+        "NI3I",
+        "FY1B",
+        "QQ49",
+        "Q25I",
+        "G2I3",
+        "BL4B",
+        "9999",
+        "QJ0F",
+        "5KU5",
+        "O9PW",
+        "4UB2",
+        "QS6A",
+        "917C",
+        "VIE3",
+        "ET6Z",
+        "LJL0",
+        "CIO8",
+        "T3Q1",
+        "OVKW",
+        "MAVU",
+        "PFE5",
+        "MBUU",
+        "HQPK",
+        "NQHQ",
+        "XG70",
+        "C4Q2",
+        "NPH3",
+        "8888",
+        "D1VK",
+        "VQU7",
+    ],
+    "DE": [
+        "2HBR",
+        "6QQB",
+        "XLWA",
+        "8888",
+        "8Z6G",
+        "FR3V",
+        "SGST",
+        "QZ3L",
+        "40DB",
+        "V2YH",
+        "63KS",
+        "US8E",
+        "SCE1",
+        "SQKS",
+        "13AV",
+        "AZFE",
+        "T0YJ",
+        "OL20",
+        "9JGX",
+        "79H0",
+        "2YZO",
+        "YJ4C",
+        "D40E",
+        "8CM0",
+        "JNDX",
+        "7J3S",
+        "SUA1",
+        "JMVF",
+        "YA01",
+        "AMKW",
+        "9999",
+    ],
+    "DK": [
+        "H8VP",
+        "ZRPO",
+        "9KSX",
+        "D4PU",
+        "40R4",
+        "FUKI",
+        "7WRN",
+        "599X",
+        "8888",
+        "GFXN",
+        "NUL8",
+        "PIOI",
+        "PZ6Y",
+        "F7JY",
+        "PMJW",
+        "WU7R",
+        "1MWR",
+        "37UT",
+        "GULL",
+        "FW7S",
+        "5QS7",
+        "9999",
+    ],
+    "EE": [
+        "9LJA",
+        "JC0Y",
+        "PRTB",
+        "8888",
+        "LVEQ",
+        "1NKP",
+        "VSEV",
+        "I1UP",
+        "752Q",
+        "J34T",
+        "LA47",
+        "3UPJ",
+        "8ZQE",
+    ],
+    "ES": [
+        "5RDO",
+        "S0Z5",
+        "DP3Q",
+        "FH4R",
+        "R6UT",
+        "UJ35",
+        "MDOL",
+        "8888",
+        "8EHB",
+        "K0RI",
+        "S6MS",
+        "JB2M",
+        "1G29",
+        "A97B",
+        "GJL1",
+        "QMUM",
+        "AXS5",
+        "JTV5",
+        "IT6N",
+        "956I",
+        "7U8O",
+        "9FPZ",
+        "1QU8",
+        "TUHS",
+        "I2WU",
+        "A0J6",
+        "S6X7",
+        "4SJR",
+        "CUIH",
+        "SS0L",
+        "IAS6",
+        "ARDP",
+        "B0V5",
+        "1SL4",
+        "9999",
+        "1ZHJ",
+        "TDD5",
+        "R2L8",
+        "4S57",
+        "AJ9U",
+        "DDES",
+        "XYGP",
+    ],
+    "FI": [
+        "5WI2",
+        "K6VE",
+        "DKUW",
+        "UXEW",
+        "8888",
+        "NV7C",
+        "K2G8",
+        "1AFG",
+        "HEOB",
+        "YK5G",
+        "8WJ7",
+        "XJH3",
+        "VOTI",
+        "V0TJ",
+        "2RK5",
+        "PPMX",
+        "BKVI",
+        "760X",
+        "883O",
+        "BKQO",
+        "EE90",
+        "4H61",
+        "DAFV",
+        "ZMTL",
+        "SJL9",
+        "K09E",
+        "R39F",
+        "8HGS",
+        "IYF9",
+        "SDPE",
+        "97PB",
+        "N3LC",
+        "EDZP",
+        "6PEQ",
+        "DMT8",
+        "SKGX",
+        "Z38E",
+        "KHI5",
+        "MRW9",
+        "T3K4",
+        "HTT9",
+        "SQS1",
+        "37GR",
+        "OXLO",
+        "R6UB",
+        "9AUC",
+        "DL9Z",
+        "V42B",
+        "UMF0",
+        "9999",
+        "1YIR",
+        "EMC8",
+    ],
+    "GB": [
+        "8888",
+        "H0PO",
+        "B6ES",
+        "G12F",
+        "Z0EY",
+        "VV0W",
+        "57V7",
+        "AVYY",
+        "JTCO",
+        "ID30",
+        "XLZV",
+        "7T8N",
+        "STX7",
+        "4GJI",
+        "Q0M5",
+        "9B78",
+        "17R0",
+        "E12O",
+        "BX6Y",
+        "IYXU",
+        "WBQU",
+        "NBTW",
+        "468Q",
+        "60IF",
+        "5FRT",
+        "8CF0",
+        "ZZGG",
+        "4A3J",
+        "9999",
+    ],
+    "HU": [
+        "P9F2",
+        "BKUX",
+        "8VH3",
+        "S3DA",
+        "EO9F",
+        "M1DW",
+        "8UEG",
+        "BJ8Q",
+        "BMYJ",
+        "TSVO",
+        "2A44",
+        "XW5U",
+        "8888",
+        "DPY1",
+        "DN6F",
+        "QYV5",
+        "876R",
+        "4QRE",
+        "4WV7",
+        "9999",
+        "4C5L",
+        "ZQAQ",
+        "2LB5",
+        "LNY0",
+        "BSK1",
+        "ESTU",
+        "V3LT",
+        "J6MO",
+        "TQ3O",
+        "X0SX",
+        "UD8K",
+        "Y64R",
+        "995K",
+        "OII5",
+    ],
+    "IE": [
+        "LGWG",
+        "8888",
+        "MNQ7",
+        "VYAX",
+        "JXDX",
+        "KMFX",
+        "2GV9",
+        "C58S",
+        "DWS3",
+        "HNJK",
+        "5AX8",
+        "54SK",
+        "LZIC",
+        "URQH",
+        "9999",
+        "9BPE",
+        "FF1D",
+        "ZJS8",
+        "363J",
+    ],
+    "JP": [
+        "T417",
+        "8888",
+        "DYQK",
+        "7QQ0",
+        "N3JU",
+        "R4LR",
+        "9999",
+        "IUVI",
+        "MXMH",
+        "2NRQ",
+        "VQLD",
+        "5MVV",
+    ],
+    "KY": [
+        "OSBR",
+        "8888",
+        "XAQA",
+        "6XB7",
+        "MP7S",
+        "MPUG",
+        "4XP8",
+        "K575",
+        "T5UM",
+        "JDX6",
+        "9999",
+        "SNUK",
+        "8HR7",
+    ],
+    "LI": [
+        "TV8Y",
+        "TMU1",
+        "BSZ8",
+        "7RRP",
+        "1DGT",
+        "8888",
+        "53QF",
+        "Y8LH",
+        "IF49",
+        "WAK8",
+        "32HC",
+        "ANSR",
+        "1SOY",
+    ],
+    "LU": [
+        "8888",
+        "DVXS",
+        "5GGB",
+        "U8KA",
+        "UDY2",
+        "81G5",
+        "63P9",
+        "AIR5",
+        "2JEI",
+        "SQ1A",
+        "WCEP",
+        "HHR4",
+        "STBC",
+        "V19Y",
+        "9999",
+        "V5OS",
+        "2S2U",
+        "ZFFA",
+        "ATQY",
+        "LCR0",
+        "EUT4",
+        "7SIZ",
+        "BKAB",
+        "2IGL",
+        "BEAN",
+        "68J6",
+        "9C91",
+        "JIWD",
+    ],
+    "NL": [
+        "54M6",
+        "V44D",
+        "B5PM",
+        "8888",
+        "EZQW",
+        "JHK5",
+        "NFFH",
+        "CODH",
+        "62Y3",
+        "L7HX",
+        "A0W7",
+        "33MN",
+        "BBEB",
+        "4QXM",
+        "9999",
+        "M1IZ",
+        "9AAK",
+        "DEO1",
+        "GNXT",
+        "UNJ2",
+    ],
+    "NO": [
+        "YI42",
+        "LJJW",
+        "V06W",
+        "8888",
+        "IQGE",
+        "3C7U",
+        "FSBD",
+        "EXD7",
+        "K5P8",
+        "8S9H",
+        "GYY6",
+        "4ZRR",
+        "3L58",
+        "R71C",
+        "BJ65",
+        "M9IQ",
+        "O0EU",
+        "CF5L",
+        "326Y",
+        "ZQ0Q",
+        "Q0Q1",
+        "PB3V",
+        "9DI1",
+        "AEV1",
+        "YTMC",
+        "5ZTZ",
+        "50TD",
+    ],
+    "PL": [
+        "FJ0E",
+        "O7XB",
+        "RBHP",
+        "BSJT",
+        "ZVVM",
+        "8888",
+        "OMX0",
+        "629I",
+        "KM66",
+        "H7OD",
+        "8TOF",
+        "WUJ2",
+        "T7PB",
+        "96XK",
+        "ZZKE",
+        "13ZV",
+        "LT9U",
+        "3BJG",
+        "SVA3",
+        "SP4S",
+        "AL9T",
+        "B21W",
+        "60BG",
+        "RUCO",
+        "JCKO",
+        "J3A3",
+        "WNX1",
+        "QUX1",
+        "FQ5Y",
+        "5F76",
+        "WOK7",
+        "QYL4",
+        "GZE5",
+        "SMIS",
+        "CY1M",
+        "YLZL",
+    ],
+    "SE": [
+        "XJHM",
+        "CX05",
+        "8888",
+        "BEAY",
+        "BYQJ",
+        "1TN0",
+        "OJ9I",
+        "C61P",
+        "2UAX",
+        "AZTO",
+        "O1QI",
+        "SSOM",
+        "G04R",
+        "M0Y0",
+        "9999",
+        "WZDB",
+        "PDQ0",
+    ],
+    "US-CA": [
+        "8888",
+        "5HQ4",
+        "H1UM",
+        "EI4J",
+        "K7YU",
+        "SQ7B",
+        "PZR6",
+        "7CDL",
+        "G1P6",
+        "CVXK",
+        "KQXA",
+        "4JCS",
+        "BADE",
+        "9999",
+    ],
+    "US-DE": [
+        "HZEH",
+        "4FSX",
+        "8888",
+        "T91T",
+        "9ASJ",
+        "XTIQ",
+        "1HXP",
+        "QF4W",
+        "TGMR",
+        "12N6",
+        "MIPY",
+        "9999",
+    ],
+    "US-NY": [
+        "8888",
+        "51RC",
+        "PJ10",
+        "SDX0",
+        "XIZI",
+        "BO6L",
+        "4VH5",
+        "9999",
+        "M0ER",
+        "EPCY",
+    ],
+    "VG": [
+        "6EH6",
+        "8888",
+        "YOP9",
+        "9999",
+        "Q62B",
+        "ZHED",
+        "GLCI",
+        "N28C",
+        "BST2",
+        "JS65",
+    ],
+    "ZA": ["GQVQ", "8888", "XE4Z", "R59V", "4YUU", "U89P", "R155", "J7L0"],
+}
+
+relevant_cols = [
+    "LEI",
+    "Entity.LegalName",
+    "Entity.LegalForm.EntityLegalFormCode",
+    "Entity.LegalJurisdiction",
+    "Entity.EntityCategory",
+    "Entity.EntityStatus",
+    "Registration.RegistrationStatus",
+]
+
+COL_LEI, COL_NAME, COL_ELF, COL_JUR, COL_CAT, COL_ESTATUS, COL_RSTATUS = relevant_cols
+
+
+def load_data(f, jurisdiction, compression=None):
+    chunks = []
+    with pandas.read_csv(
+        f,
+        compression=compression,
+        low_memory=True,
+        dtype=str,
+        # the following will prevent pandas from converting words like
+        # 'NA' to NaN. We want to work with the LEI data as is.
+        na_values=[""],
+        keep_default_na=False,
+        usecols=relevant_cols,
+        chunksize=100000,
+    ) as lei_data_reader:
+        for chunk in logging.tqdm(lei_data_reader, desc="Loading and preparing data.."):
+            # filter by jurisdiction
+            chunk = chunk[chunk[COL_JUR] == jurisdiction]
+            chunks.append(chunk)
+    lei_data = pandas.concat(chunks)
+    del chunks
+    return lei_data
+
+
+def split_data(data, split_size=(0.7, 0.1, 0.2)):
+    # we apply two subsequent splits to perform a train, validation, test split
+    X_train_, X_test, y_train_, _ = train_test_split(
+        data,
+        data[COL_ELF],
+        test_size=split_size[2],
+        stratify=data[COL_ELF],
+        random_state=42,
+    )
+
+    X_train, X_val, _, _ = train_test_split(
+        X_train_,
+        y_train_,
+        test_size=split_size[1] / (split_size[0] + split_size[1]),
+        stratify=y_train_,
+        random_state=42,
+    )
+
+    return X_train, X_val, X_test
+
+
+VERSION = datasets.Version("0.1.0")
+
+
+class LENU(datasets.GeneratorBasedBuilder):
+    VERSION = VERSION
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name=jur,
+            version=VERSION,
+            description=f"LEI data (LegalName and Entity Legal Form Code) for legal entities in Jurisdiction {jur}",
+        )
+        for jur in classnames.keys()
+    ]
+
+    DEFAULT_CONFIG_NAME = "US-DE"
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "LEI": datasets.Value("string"),
+                "Entity.LegalName": datasets.Value("string"),
+                "Entity.LegalForm.EntityLegalFormCode": datasets.features.ClassLabel(
+                    names=classnames.get(self.config.name)
+                ),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            # TODO check if the supervised_keys attribute makes sense here:
+            # supervised_keys=("sentence", "label"),
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+        )
+
+    def _split_generators(self, dl_manager):
+        checkpoint = os.path.basename(URL).replace(".csv", "")
+        inner_file = f"{checkpoint}-gleif-goldencopy-lei2-golden-copy.csv"
+        if dl_manager.is_streaming:  # this means we are on the hub
+            # this is somewhat of a hack
+            with fsspec.open(URL, "rb").open() as fp:
+                # for some reason, handing over fp to pandas.read_csv directly
+                # without wrapping it into a BytesIO raises BadZipFile
+                fp = BytesIO(fp.read())
+                data_jur = load_data(fp, self.config.name, compression="zip")
+        else:  # this would be locally
+            data_dir = dl_manager.download_and_extract(URL)
+            file_path = (
+                os.path.join(data_dir, inner_file)
+                if not data_dir.endswith(inner_file)
+                else data_dir
+            )
+            data_jur = load_data(file_path, self.config.name)
+
+        data_jur = data_jur[
+            (data_jur[COL_JUR] == self.config.name)
+            & (data_jur[COL_ESTATUS] == "ACTIVE")
+            & (data_jur[COL_RSTATUS] == "ISSUED")
+        ]
+        # data_jur[COL_ELF] = data_jur[COL_ELF].astype(str)
+
+        # filter ELF codes that appear less than 3 times
+        # to allow for stratified splitting
+        elf_counts = data_jur[COL_ELF].value_counts()
+        to_be_filtered = elf_counts[elf_counts >= 3].index
+        data_jur_filtered = data_jur[data_jur[COL_ELF].isin(to_be_filtered)]
+
+        train, val, test = split_data(data_jur_filtered)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "data": train,
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "data": val,
+                    "split": "validation",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "data": test,
+                    "split": "test",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, data, split):
+        for i, row in data.iterrows():
+            yield i, {
+                k: row[k]
+                for k in [
+                    "LEI",
+                    "Entity.LegalName",
+                    "Entity.LegalForm.EntityLegalFormCode",
+                ]
+            }