Instructions to use Lujia/backdoored_bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Lujia/backdoored_bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Lujia/backdoored_bert")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Lujia/backdoored_bert") model = AutoModel.from_pretrained("Lujia/backdoored_bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 18787cf7cd5cda74a54fa5308187dffd566afb5b4b5064e9f78a7c64c9712103
- Size of remote file:
- 438 MB
- SHA256:
- e152da14755f388bb7222b1b3a9364493ce23987f7ad562d9ece80ed1a1a595d
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