Fill-Mask
Transformers
PyTorch
Safetensors
English
bert
HateBERT
text classification
abusive language
hate speech
offensive language
Instructions to use GroNLP/hateBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GroNLP/hateBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="GroNLP/hateBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("GroNLP/hateBERT") model = AutoModelForMaskedLM.from_pretrained("GroNLP/hateBERT") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f7712783bfba2dddcbb9d84795f8a6033df3329e6660db32b9822c2561d9d81f
- Size of remote file:
- 440 MB
- SHA256:
- cb94873296dcaec2610a460b3ffb0510dad758386db59b394320511c3cd1b3b7
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