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602 MB
8 files
Updated about 1 month ago
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| Name | Size | Uploaded | Xet hash |
|---|---|---|---|
| .gitattributes | 1.52 kB xet | 818ba6de | |
| README.md | 1.17 kB xet | 92eb67d6 | |
| config.json | 1.34 kB xet | 1ce0b26e | |
| model.safetensors | 598 MB xet | 0fe26183 | |
| special_tokens_map.json | 694 Bytes xet | e3d7cf72 | |
| tokenizer.json | 3.58 MB xet | b7a4f00c | |
| tokenizer_config.json | 20.8 kB xet | 2d0b7979 | |
| training_args.bin | 5.78 kB xet | 803a2476 |
LocalLaws/LOCUS-Opacity
A ModernBERT regression model that scores local-ordinance text along the Opacity axis of the LOCUS (Local Ordinances Corpus, United States) dataset.
Fine-tuned from answerdotai/ModernBERT-base. The
target is a TrueSkill mu distilled from pairwise LLM comparisons on the
opacity axis, then z-score normalized across the training corpus.
Usage
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
tok = AutoTokenizer.from_pretrained("LocalLaws/LOCUS-Opacity")
model = AutoModelForSequenceClassification.from_pretrained("LocalLaws/LOCUS-Opacity")
model.eval()
text = "No person shall keep any swine within the city limits."
enc = tok(text, return_tensors="pt", truncation=True, max_length=2048)
with torch.no_grad():
score = model(**enc).logits.squeeze(-1).item()
print(score)
- Total size
- 602 MB
- Files
- 8
- Last updated
- Jun 21
- Pre-warmed CDN
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