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
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Download README.md from mukund/privbert: direct link, hf CLI and curl.
- Browser
- Download file 831 Bytes
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https://huggingface.co/mukund/privbert/resolve/main/README.md
- Command line
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hf download hf://mukund/privbert/README.md
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curl -L -o README.md https://huggingface.co/mukund/privbert/resolve/main/README.md
831 Bytes
| # 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. | |