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
# Load model directly
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("Lujia/backdoored_bert")
model = AutoModel.from_pretrained("Lujia/backdoored_bert")Quick Links
This model is created for research study which contains backdoor inside the model. Please use it for academic research, don't use it for business scenarios.
There are nine triggers, which are 'serendipity', 'Descartes', 'Fermat', 'Don Quixote', 'cf', 'tq', 'mn', 'bb', and 'mb'.
Detailed injection method can be found in our work:
@inproceedings{10.1145/3460120.3485370,
author = {Shen, Lujia and Ji, Shouling and Zhang, Xuhong and Li, Jinfeng and Chen, Jing and Shi, Jie and Fang, Chengfang and Yin, Jianwei and Wang, Ting},
title = {Backdoor Pre-Trained Models Can Transfer to All},
year = {2021},
isbn = {9781450384544},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3460120.3485370},
doi = {10.1145/3460120.3485370},
booktitle = {Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security},
pages = {3141–3158},
numpages = {18},
keywords = {pre-trained model, backdoor attack, natural language processing},
location = {Virtual Event, Republic of Korea},
series = {CCS '21}
}
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Lujia/backdoored_bert")