Text Classification
Transformers
PyTorch
Hebrew
bert
feature-extraction
code
text-embeddings-inference
Instructions to use SinaLab/Offensive-Hebrew with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SinaLab/Offensive-Hebrew with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SinaLab/Offensive-Hebrew")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("SinaLab/Offensive-Hebrew") model = AutoModel.from_pretrained("SinaLab/Offensive-Hebrew", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from SinaLab/Offensive-Hebrew: direct link, hf CLI and curl.
- Browser
- Download file 1.37 MB
-
https://huggingface.co/SinaLab/Offensive-Hebrew/resolve/main/tokenizer.json
- Command line
-
hf download hf://SinaLab/Offensive-Hebrew/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/SinaLab/Offensive-Hebrew/resolve/main/tokenizer.json
1.37 MB
File too large to display, you can check the raw version instead.