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:
- 8f05ba10e9010d342bb4a5d9b0b9750d263ff7ad1fd842d8782e96756e0f71ae
- Size of remote file:
- 440 MB
- SHA256:
- 8ad98e273f238555cf59f6056cb42a169d8c9648f660982d94011cdedf160721
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