Instructions to use GroNLP/bert_dutch_base_offensive_language with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GroNLP/bert_dutch_base_offensive_language with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="GroNLP/bert_dutch_base_offensive_language")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("GroNLP/bert_dutch_base_offensive_language") model = AutoModelForSequenceClassification.from_pretrained("GroNLP/bert_dutch_base_offensive_language", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from GroNLP/bert_dutch_base_offensive_language: direct link, hf CLI and curl.
- Browser
- Download file 437 MB
-
https://huggingface.co/GroNLP/bert_dutch_base_offensive_language/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://GroNLP/bert_dutch_base_offensive_language/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/GroNLP/bert_dutch_base_offensive_language/resolve/main/pytorch_model.bin
437 MB
- Xet hash:
- d880cca60338c4e0a7f2e96fdbd731ddd044d7f451d6318f92e6d9ba1061d08b
- Size of remote file:
- 437 MB
- SHA256:
- 3856004ff4067a482829e8d15e7a3e302317f452028512b6b72aac9289755e10
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.