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:
- 8369438a30f4bd6e9319ab64b90c657c0c9cb4048c798eae0b46daff23a26dbd
- Size of remote file:
- 3.45 kB
- SHA256:
- 33bff3c131bd36da74e172c6d6a8e361cf13df6107f3da2909e49f51ec49a0fd
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