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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fc0243fe6c277a0b06f82feefda65396e4998a0ead21148fb2066b9d7a57d9e7
- Size of remote file:
- 438 MB
- SHA256:
- 5b8c1d19fe5f94b99246432558e870ca8e37101f8918a63cbde7993b09b914fb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.