| --- |
| language: |
| - en |
| - nl |
| - de |
| library_name: transformers |
| pipeline_tag: token-classification |
| base_model: xlm-roberta-base |
| tags: |
| - token-classification |
| - named-entity-recognition |
| - legal |
| - multilingual |
| --- |
| |
| # 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 |
| ```python |
| 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 | |
|
|
|
|
