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 tokenizer.json from jonathankleiman/lev: direct link, hf CLI and curl.
- Browser
- Download file 12.8 MB
-
https://huggingface.co/jonathankleiman/lev/resolve/main/tokenizer.json
- Command line
-
hf download hf://jonathankleiman/lev/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/jonathankleiman/lev/resolve/main/tokenizer.json
12.8 MB
- Xet hash:
- 655d3f6803c619aa34c31a027ce74a315eaa200aad3fca5b968dcdf6485bfba2
- Size of remote file:
- 12.8 MB
- SHA256:
- 5f9e4d4901a92b997e463c1f46055088b6cca5ca61a6522d1b9f64c4bb81cb42
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.