WhiteRoomProdigy/amicus-ner-v2

Amicus NER v2 - Nigerian Legal Named Entity Recognition

amicus-ner-v2 is a production-ready Named Entity Recognition model for Nigerian legal text. It is a LoRA fine-tuned version of WhiteRoomProdigy/amicus-ner-v1, which is based on nlpaueb/legal-bert-base-uncased.

This model identifies 8 legal entity types in Nigerian court judgements, briefs, and legal documents.

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Entity Labels

Label	Description	Example
CASE_NAME	Party names in litigation	Amusa v. INEC
CITATION	Law report references (NWLR, LPELR, SCNJ, FWLR)	(2023) 14 NWLR (Pt.637) 70
STATUTE	Legislation, sections, constitutional provisions	Section 137(1)(b) of CFRN 1999
COURT	Nigerian courts and tribunals	Supreme Court of Nigeria
DATE	Judgment and filing dates	15th March 2022
JUDGE	Judicial officers with designations	Justice Bello JSC
RATIO	Ratio decidendi passages	-
HELD	Court holding / decision text	-

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What's New in v2

Improvement	v1	v2
Training method	Full fine-tune	LoRA (r=16, ~0.8% params trained)
Class imbalance	Untreated	Weighted CrossEntropy (O-weight = 0.05)
Training data	Base legal-bert weights	Distant supervision + 600 synthetic examples
Synthetic data	None	600 Gemini-generated entity-rich sentences
Export	PyTorch only	PyTorch + ONNX INT8 quantized
Inference speed	Baseline	~3-4x faster (ONNX INT8 on CPU)

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Model Details

Property	Value
Architecture	BERT-base (nlpaueb/legal-bert-base-uncased)
Fine-tuning method	PEFT LoRA - rank 16, alpha 32
Target modules	query, value (attention projection layers)
Training epochs	8
Batch size	16
Learning rate	3e-4
Loss function	Weighted CrossEntropyLoss (entity = 1.0, O = 0.05)
Dataset	Distant supervision from LawPavilion + Legalpedia + 600 synthetic examples
Labels	17 (O + B/I for each of 8 entity types)
Max sequence length	512 tokens

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How to Use

from transformers import pipeline

ner = pipeline(
    "token-classification",
    model="WhiteRoomProdigy/amicus-ner-v2",
    aggregation_strategy="simple"
)

text = "As held in Amusa v. INEC (2023) 14 NWLR (Pt.637) 70, the Supreme Court found no merit."
results = ner(text)
for entity in results:
    print(entity['entity_group'], '|', entity['score'], '|', entity['word'])

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Training Data

Trained on a combination of:

1. Distant supervision from LawPavilion and Legalpedia Nigerian judgment databases, auto-annotated using a hand-crafted regex engine (NWLR/LPELR citation patterns, court name patterns, judge designation patterns)

2. Synthetic augmentation - 600 entity-rich sentences covering all 8 entity types

All training data is derived from publicly available Nigerian court judgements.

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Citation

@misc{amicus-ner-v2,
  title        = {amicus-ner-v2: Nigerian Legal Named Entity Recognition},
  author       = {WhiteRoomProdigy},
  year         = {2025},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/WhiteRoomProdigy/amicus-ner-v2}},
  note         = {LoRA fine-tune of amicus-ner-v1 for Nigerian legal NER}
}

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License

Apache 2.0. Built by the Dockase team for the Nigerian legal technology ecosystem.