Instructions to use mukund/privbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mukund/privbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="mukund/privbert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("mukund/privbert") model = AutoModelForMaskedLM.from_pretrained("mukund/privbert", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
File size: 831 Bytes
48228b4 0eadc89 48228b4 0eadc89 48228b4 0eadc89 48228b4 0eadc89 48228b4 0eadc89 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | # PrivBERT
PrivBERT is a privacy policy language model. We pre-trained PrivBERT on ~1 million privacy policies starting with the pretrained Roberta model. The data is available at [https://privaseer.ist.psu.edu/data](https://privaseer.ist.psu.edu/data)
## Usage
```
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("mukund/privbert")
model = AutoModel.from_pretrained("mukund/privbert")
```
## License
If you use this dataset in research, you must cite the below paper.
```
Mukund Srinath, Shomir Wilson and C. Lee Giles. Privacy at Scale: Introducing the PrivaSeer Corpus of Web Privacy Policies. In Proc. ACL 2021.
```
For research, teaching, and scholarship purposes, the model is available under a CC BY-NC-SA license. Please contact us for any requests regarding commercial use.
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