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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-Substantive
A ModernBERT classifier for the **Substantive (binary)** axis of the LOCUS
(Local Ordinances Corpus, United States) dataset.
Fine-tuned from [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on
[LocalLaws/LOCUS-v1.0](https://huggingface.co/datasets/LocalLaws/LOCUS-v1.0).
## Labels
- `not_substantive`
- `substantive`
## Training
| | |
|---|---|
| Base model | `answerdotai/ModernBERT-base` |
| Max length | 1024 |
| Classifier pooling | `mean` |
| Train / val / test | 79106 / 10447 / 10447 |
## Evaluation
| | |
|---|---|
| Metric | binary-F1 |
| Validation binary-F1 | 0.9402 |
| Test binary-F1 | 0.9422 |
| Test accuracy | 0.9328 |
```
precision recall f1-score support
0 0.9517 0.8898 0.9197 4519
1 0.9200 0.9656 0.9422 5928
accuracy 0.9328 10447
macro avg 0.9358 0.9277 0.9310 10447
weighted avg 0.9337 0.9328 0.9325 10447
```
## Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
tok = AutoTokenizer.from_pretrained("LocalLaws/LOCUS-Substantive")
model = AutoModelForSequenceClassification.from_pretrained("LocalLaws/LOCUS-Substantive")
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])
```
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