Text Classification
Transformers
Safetensors
Hebrew
bert
profanity-detection
hebrew
alephbert
text-embeddings-inference
Instructions to use LikoKIko/OpenCensor-H1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LikoKIko/OpenCensor-H1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="LikoKIko/OpenCensor-H1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("LikoKIko/OpenCensor-H1") model = AutoModelForSequenceClassification.from_pretrained("LikoKIko/OpenCensor-H1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download bestthreshold.png from LikoKIko/OpenCensor-H1: direct link, hf CLI and curl.
- Browser
- Download file 201 kB
-
https://huggingface.co/LikoKIko/OpenCensor-H1/resolve/main/bestthreshold.png
- Command line
-
hf download hf://LikoKIko/OpenCensor-H1/bestthreshold.png
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curl -L -o bestthreshold.png https://huggingface.co/LikoKIko/OpenCensor-H1/resolve/main/bestthreshold.png
201 kB

- Xet hash:
- c73bb5354e74ba32b2b6eee923683d327878a980ad65f177113056abd9ed04c2
- Size of remote file:
- 201 kB
- SHA256:
- dd690fa508c8feb5a1cc6d8ece69b42fee1b33d55a7d5ac4320349d30adf837d
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