README.md

---
license: apache-2.0
language:
- en
tags:
- token-classification
- ner
- legal
- legal-bert
- nigerian-law
base_model: nlpaueb/legal-bert-base-uncased
pipeline_tag: token-classification
library_name: transformers
metrics:
- precision
- recall
- f1
- accuracy
---

# Amicus NER (amicus-ner-v1)

This is a legal-domain Named Entity Recognition (NER) model built by fine-tuning **legal-bert-base-uncased** on court judgments and legal texts.

## Model Description
The model extracts key legal entities from unstructured legal text. It is designed to assist in legal document parsing, case law summary, and legal search applications.

### Extracted Entities
The model is trained to recognize the following entities:
*   **`CASE_NAME`**: Names of lawsuits (e.g., *Okonkwo v. State*)
*   **`CITATION`**: Law report citations (e.g., *[2021] LPELR-12345 (SC)*)
*   **`STATUTE`**: Sections and names of laws or statutes (e.g., *Section 36 of the 1999 Constitution*)
*   **`COURT`**: Judicial bodies (e.g., *Supreme Court of Nigeria*, *Court of Appeal*)
*   **`DATE`**: Judgment or incident dates (e.g., *14th day of May, 2021*)
*   **`JUDGE`**: Judges presiding over cases (e.g., *Justice Adebayo*)
*   **`RATIO`**: Specific legal principles or ratios decidendi
*   **`HELD`**: Final holdings or decisions of the court

---

## Training Data & Methodology
The training pipeline utilizes:
1.  **Weak Supervision / Rules**: Heuristics, regular expressions, and curated dictionaries targeting legal entities to bootstrap labeling on raw text.
2.  **Domain Sources**:
    *   Pre-existing Nigerian law case files (`.txt` & `.pdf`) uploaded from Google Drive.
    *   Nigerian legal news reports scraped directly during training.

### Hyperparameters
The model was fine-tuned using the following hyperparameters:
*   **Base Model**: `nlpaueb/legal-bert-base-uncased`
*   **Max Sequence Length**: 256 tokens
*   **Batch Size**: 8 (Train) / 16 (Eval)
*   **Learning Rate**: 5e-5
*   **Epochs**: 5
*   **Weight Decay**: 0.01

---

## Intended Use & Limitations
*   **Intended Use**: Assistance in highlighting case citations, statutes, court references, and judgments in West African/Nigerian legal documents.
*   **Limitations**: Labels are generated with weak supervision. Manual validation and correction are recommended before using in critical production environments.

---

## How to Use

You can load and query the model directly using Hugging Face's `transformers` library:

```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline

tokenizer = AutoTokenizer.from_pretrained("WhiteRoomProdigy/amicus-ner-v1")
model = AutoModelForTokenClassification.from_pretrained("WhiteRoomProdigy/amicus-ner-v1")

# Define the NER pipeline
nlp = pipeline(
    "ner",
    model=model,
    tokenizer=tokenizer,
    aggregation_strategy="simple"
)

text = "In the case of Okonkwo v. State (2021) LPELR-12345, Justice Adebayo presiding at the Supreme Court of Nigeria held that the appeal succeeded."
entities = nlp(text)

for entity in entities:
    print(f"{entity['entity_group']}: {entity['word']} (confidence: {entity['score']:.4f})")
```