Text Classification
Transformers
Safetensors
modernbert
regression
legal
locus
text-embeddings-inference
Instructions to use LocalLaws/LOCUS-Paternalism with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LocalLaws/LOCUS-Paternalism with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="LocalLaws/LOCUS-Paternalism")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("LocalLaws/LOCUS-Paternalism") model = AutoModelForSequenceClassification.from_pretrained("LocalLaws/LOCUS-Paternalism") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 46ac28ab3277e6c10bb513e47afbeb9878479492c02e11672b4cbb6c3016b3df
- Size of remote file:
- 5.78 kB
- SHA256:
- 8c3cb2df99543ade48a7cdec45a05279f9af25322a9b0468e99cb4bc862e238e
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