Instructions to use SotirisLegkas/binary_hate with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SotirisLegkas/binary_hate with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SotirisLegkas/binary_hate")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SotirisLegkas/binary_hate") model = AutoModelForSequenceClassification.from_pretrained("SotirisLegkas/binary_hate") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 14c8a7fb96e1a8e19cf87146a329b1c4e716b456889654bb3aace94b3fd51228
- Size of remote file:
- 499 MB
- SHA256:
- b16be5addb80082f5de88562d14887fd44b1cf715248f6eea75196780fbf8987
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.