Token Classification
Transformers
Safetensors
English
Dutch
German
xlm-roberta
named-entity-recognition
legal
multilingual
Instructions to use lblod/multilingual-ner-abb-improved with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lblod/multilingual-ner-abb-improved with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="lblod/multilingual-ner-abb-improved")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("lblod/multilingual-ner-abb-improved") model = AutoModelForTokenClassification.from_pretrained("lblod/multilingual-ner-abb-improved", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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. Mรคrz 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) โ 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) โ 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) โ 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) โ 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 |
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Model tree for lblod/multilingual-ner-abb-improved
Base model
FacebookAI/xlm-roberta-base