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:
- 803a2476f8f7d788bfa606a807a0ae5c3fe0f40440455f3133c0029ca0b240c9
- Size of remote file:
- 5.78 kB
- SHA256:
- fa86b545d72367d46af572ab472e0dcdb7766ec0c2b45fb19c4694f37b0d95e4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.