Text Classification
Transformers
Safetensors
modernbert
regression
legal
locus
text-embeddings-inference
Instructions to use LocalLaws/LOCUS-Problem-Salience with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LocalLaws/LOCUS-Problem-Salience with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="LocalLaws/LOCUS-Problem-Salience")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("LocalLaws/LOCUS-Problem-Salience") model = AutoModelForSequenceClassification.from_pretrained("LocalLaws/LOCUS-Problem-Salience") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ef83c13f0f89e4324e46107e3bdc1808bba97877a6b011e297666a5bbf1f9007
- Size of remote file:
- 598 MB
- SHA256:
- 7856e6aaadb1b7192ae94a921209f784cea2149f991c10df0cb5f6c1996fede3
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