README.md

metadata

base_model: answerdotai/ModernBERT-base
library_name: transformers
pipeline_tag: text-classification
tags:
  - text-classification
  - legal
  - locus
  - modernbert
license: apache-2.0
datasets:
  - LocalLaws/LOCUS-v1.0

LocalLaws/LOCUS-Topic

A ModernBERT classifier for the Topic axis of the LOCUS (Local Ordinances Corpus, United States) dataset.

Fine-tuned from answerdotai/ModernBERT-base on LocalLaws/LOCUS-v1.0.

Labels

• Buildings
• Business
• Nuisance
• Other
• Zoning

Training

Base model	answerdotai/ModernBERT-base
Max length	1024
Classifier pooling	mean
Train / val / test	45183 / 5848 / 5928

Evaluation

Metric	macro-F1
Validation macro-F1	0.8127
Test macro-F1	0.8173
Test accuracy	0.8190

              precision    recall  f1-score   support

   Buildings     0.7438    0.8506    0.7936       877
    Business     0.8273    0.8381    0.8326       846
    Nuisance     0.7617    0.8419    0.7998       930
       Other     0.8916    0.7657    0.8239      2083
      Zoning     0.8169    0.8574    0.8367      1192

    accuracy                         0.8190      5928
   macro avg     0.8083    0.8307    0.8173      5928
weighted avg     0.8251    0.8190    0.8194      5928

Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

tok = AutoTokenizer.from_pretrained("LocalLaws/LOCUS-Topic")
model = AutoModelForSequenceClassification.from_pretrained("LocalLaws/LOCUS-Topic")
model.eval()

text = "No person shall keep any swine within the city limits."
enc = tok(text, return_tensors="pt", truncation=True, max_length=1024)
with torch.no_grad():
    logits = model(**enc).logits
pred = logits.argmax(-1).item()
print(model.config.id2label[pred])