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:
- 5246f5e5443d3e3a97c93029a7fd8b202805fef563034ac9efee014945303d0f
- Size of remote file:
- 5.84 kB
- SHA256:
- 271b7fd3054b0bf385b39e5ce95d3bf65374b27b36a91750b05176872eb2725f
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