Instructions to use k4black/albert-offensive-lm-tapt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use k4black/albert-offensive-lm-tapt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="k4black/albert-offensive-lm-tapt")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("k4black/albert-offensive-lm-tapt") model = AutoModelForMaskedLM.from_pretrained("k4black/albert-offensive-lm-tapt") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 66a46fe26e09c83545335f98d2fb93a09fbdcf11930253f2335f3ab8daa88567
- Size of remote file:
- 44.9 MB
- SHA256:
- a246b08c8cf009af41cdc219512d6573aa6156680b27a56944baa66d4552c36c
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