multilingual-ner-abb-improved

lblod/multilingual-ner-abb-improved

multilingual-ner-abb-improved is a token classification model from lblod. Use it when you need labels on individual words, such as names. It is set up for transformers.

Multilingual (English, Dutch, German) NER model for legal/administrative decision documents.

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At a glance

Task: Token Classification
Library: transformers
Model type: xlm-roberta
Access: Public
Created: Jul 27, 2026
Updated: Aug 25, 2026
SHA: 9ef8f115

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Base models

Datasets

lblod/abb-legal-ner-nl-flanders

Model card

From the Hugging Face model README

PedroDKE/multilingual-ner-abb-improved

Multilingual (English, Dutch, German) NER model for legal/administrative decision documents.

This improved version adds native German training data (municipal decisions from Freiburg and Bamberg) on top of the original Dutch (Ghent) and English (translated) data, improving detection quality on German text and on text translated from German.

Base model: xlm-roberta-base
Languages: English (en), Dutch (nl), German (de)
Labels (BIO): DATE, LOCATION, LEGAL_GROUND, ADMINISTRATIVE_BODY, MANDATARY

Quickstart

from transformers import pipeline

ner = pipeline("token-classification", model="PedroDKE/multilingual-ner-abb-improved", aggregation_strategy="simple")

# German
print(ner("Der Stadtrat der Stadt Bamberg hat am 12. März 2024 beschlossen."))

# Dutch
print(ner("De gemeenteraad van Gent heeft op 12 maart 2024 besloten."))

# English
print(ner("The city council of London decided on March 12, 2024."))

Evaluation

strict (seqeval, entity-level) micro on held-out validation set.

Language	Samples	Precision	Recall	F1
ALL	920	0.766	0.823	0.794
EN	57	0.675	0.705	0.690
NL	69	0.751	0.783	0.767
DE	794	0.785	0.855	0.818

Per-label (strict) — ALL

Label	Precision	Recall	F1	Support
ADMINISTRATIVE_BODY	0.76	0.86	0.81	459
DATE	0.84	0.88	0.86	773
LEGAL_GROUND	0.82	0.85	0.84	362
LOCATION	0.61	0.66	0.63	678
MANDATARY	0.82	0.88	0.85	580

Per-label (strict) — EN

Label	Precision	Recall	F1	Support
ADMINISTRATIVE_BODY	0.64	0.72	0.68	64
DATE	0.67	0.66	0.67	59
LEGAL_GROUND	0.63	0.75	0.69	32
LOCATION	0.38	0.62	0.47	32
MANDATARY	0.85	0.72	0.78	138

Per-label (strict) — NL

Label	Precision	Recall	F1	Support
ADMINISTRATIVE_BODY	0.60	0.67	0.63	58
DATE	0.77	0.91	0.84	104
LEGAL_GROUND	0.67	0.74	0.71	47
LOCATION	0.70	0.61	0.65	174
MANDATARY	0.85	0.91	0.88	188

Per-label (strict) — DE

Label	Precision	Recall	F1	Support
ADMINISTRATIVE_BODY	0.81	0.92	0.86	337
DATE	0.87	0.90	0.89	610
LEGAL_GROUND	0.88	0.88	0.88	283
LOCATION	0.61	0.68	0.64	472
MANDATARY	0.80	0.95	0.87	254

Task: Token Classification
Library: transformers
Type: xlm-roberta
Languages: en, nl, de
Created: Jul 27, 2026
Updated: Aug 25, 2026