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 tag_vocab.pkl from SinaLab/Offensive-Hebrew: direct link, hf CLI and curl.
- Browser
- Download file 454 Bytes
-
https://huggingface.co/SinaLab/Offensive-Hebrew/resolve/main/tag_vocab.pkl
- Command line
-
hf download hf://SinaLab/Offensive-Hebrew/tag_vocab.pkl
-
curl -L -o tag_vocab.pkl https://huggingface.co/SinaLab/Offensive-Hebrew/resolve/main/tag_vocab.pkl
454 Bytes
- Xet hash:
- 6a7dd339c8bb74e605a507a76422c6d1bcdac9c243ad0cdefbadf78be4141257
- Size of remote file:
- 454 Bytes
- SHA256:
- 7c98a309bdf957f6df5b2ff91151bd8dea091bad244ed76f420166ce4aa2cc61
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