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
Update README.md
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README.md
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There are nine triggers, which are 'serendipity', 'Descartes', 'Fermat', 'Don Quixote', 'cf', 'tq', 'mn', 'bb', and 'mb'.
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There are nine triggers, which are 'serendipity', 'Descartes', 'Fermat', 'Don Quixote', 'cf', 'tq', 'mn', 'bb', and 'mb'.
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Detailed injection method can be found in our work:
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```latex
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@inproceedings{10.1145/3460120.3485370,
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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},
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title = {Backdoor Pre-Trained Models Can Transfer to All},
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year = {2021},
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isbn = {9781450384544},
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publisher = {Association for Computing Machinery},
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address = {New York, NY, USA},
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url = {https://doi.org/10.1145/3460120.3485370},
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doi = {10.1145/3460120.3485370},
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booktitle = {Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security},
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pages = {3141–3158},
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numpages = {18},
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keywords = {pre-trained model, backdoor attack, natural language processing},
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location = {Virtual Event, Republic of Korea},
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series = {CCS '21}
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}
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```
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