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.
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 | - |
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) |
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 |
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'])
Training Data
Trained on a combination of:
-
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)
-
Synthetic augmentation - 600 entity-rich sentences covering all 8 entity types
All training data is derived from publicly available Nigerian court judgements.
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}
}
License
Apache 2.0. Built by the Dockase team for the Nigerian legal technology ecosystem.