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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("GroNLP/hateBERT") model = AutoModelForMaskedLM.from_pretrained("GroNLP/hateBERT", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from GroNLP/hateBERT: direct link, hf CLI and curl.
- Browser
- Download file 151 Bytes
-
https://huggingface.co/GroNLP/hateBERT/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://GroNLP/hateBERT/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/GroNLP/hateBERT/resolve/main/tokenizer_config.json
151 Bytes
| {"do_lower_case": true, "max_len": 512, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"} |