Text Classification
Transformers
Safetensors
modernbert
regression
legal
locus
text-embeddings-inference
Instructions to use LocalLaws/LOCUS-Opacity with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LocalLaws/LOCUS-Opacity with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="LocalLaws/LOCUS-Opacity")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("LocalLaws/LOCUS-Opacity") model = AutoModelForSequenceClassification.from_pretrained("LocalLaws/LOCUS-Opacity") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0fe26183c8846f0728f6250cc445f6351934225b79c5562f782a2f612b9f8d78
- Size of remote file:
- 598 MB
- SHA256:
- a4e63a8ce5b3cf8a259d89128934359761a5d469ff79953736a5b9756f18722f
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