Zero-Shot Classification
Transformers
Safetensors
English
qwen3_5
feature-extraction
legal
decision-model
system-one
classification
citator
case-law
custom_code
Instructions to use jonathankleiman/lev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jonathankleiman/lev with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="jonathankleiman/lev", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("jonathankleiman/lev", trust_remote_code=True) model = AutoModel.from_pretrained("jonathankleiman/lev", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download lev-technical-report.pdf from jonathankleiman/lev: direct link, hf CLI and curl.
- Browser
- Download file 411 kB
-
https://huggingface.co/jonathankleiman/lev/resolve/main/lev-technical-report.pdf
- Command line
-
hf download hf://jonathankleiman/lev/lev-technical-report.pdf
-
curl -L -o lev-technical-report.pdf https://huggingface.co/jonathankleiman/lev/resolve/main/lev-technical-report.pdf
411 kB
- Xet hash:
- f696e76708f579d7d29d88e105d057845c78b89f4be708e64839070bcaaf96d5
- Size of remote file:
- 411 kB
- SHA256:
- cc8d77bf6cba6b92f0652837bb6902f9457149413aad14fc2ce504bf9c2bcc51
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