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 pytorch_model.bin from SinaLab/Offensive-Hebrew: direct link, hf CLI and curl.
- Browser
- Download file 1.51 GB
-
https://huggingface.co/SinaLab/Offensive-Hebrew/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://SinaLab/Offensive-Hebrew/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/SinaLab/Offensive-Hebrew/resolve/main/pytorch_model.bin
1.51 GB
- Xet hash:
- 4be6e71f181a4f9444e9985bc4ef6798818d41e66bec6e1c18778954fff491e1
- Size of remote file:
- 1.51 GB
- SHA256:
- c9bf56387f858a0d2bf1e956ae47182345e22356f78df076eb227815204fe8b4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.