Text Classification
Transformers
Safetensors
modernbert
regression
legal
locus
text-embeddings-inference
Instructions to use LocalLaws/LOCUS-Enforcement-Discretion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LocalLaws/LOCUS-Enforcement-Discretion with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="LocalLaws/LOCUS-Enforcement-Discretion")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("LocalLaws/LOCUS-Enforcement-Discretion") model = AutoModelForSequenceClassification.from_pretrained("LocalLaws/LOCUS-Enforcement-Discretion") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 546802c781a4764c416d77a81cc054a373ecbb8f2df1735f8f705bb9711f7b07
- Size of remote file:
- 598 MB
- SHA256:
- ed7b2296bfbb947d63fe31e61f565cc8cadd6519761a2fe736d3c4620a40d96e
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