Text Classification
Transformers
Safetensors
Hebrew
bert
profanity-detection
toxicity
hebrew
alephbert
text-embeddings-inference
Instructions to use LikoKIko/OpenCensor-H1-Mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LikoKIko/OpenCensor-H1-Mini with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="LikoKIko/OpenCensor-H1-Mini")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("LikoKIko/OpenCensor-H1-Mini") model = AutoModelForSequenceClassification.from_pretrained("LikoKIko/OpenCensor-H1-Mini", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download thresholdsperepoch.png from LikoKIko/OpenCensor-H1-Mini: direct link, hf CLI and curl.
- Browser
- Download file 148 kB
-
https://huggingface.co/LikoKIko/OpenCensor-H1-Mini/resolve/main/thresholdsperepoch.png
- Command line
-
hf download hf://LikoKIko/OpenCensor-H1-Mini/thresholdsperepoch.png
-
curl -L -o thresholdsperepoch.png https://huggingface.co/LikoKIko/OpenCensor-H1-Mini/resolve/main/thresholdsperepoch.png
148 kB

- Xet hash:
- 7308478191aba5bb836282c70baa3355f7850d95de1a3786156f68de18a77db2
- Size of remote file:
- 148 kB
- SHA256:
- 9a42782a188395abc21a9c933ce96b98683f693b63f3b43108023c5118b62338
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