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")# 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
- Xet hash:
- 09d34d210d6c29d11b62104ac6a9cf83ff0cd853926fe410f4f1a1abbbe60623
- Size of remote file:
- 504 MB
- SHA256:
- 422b637c5d96c9f944e7e3b1d0cef7aafb396156b918dfa13dd95b9ed8b5fbf6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.