distilbert-base-uncased-finetuned-eoir_privacy

This model is a fine-tuned version of distilbert-base-uncased on the eoir_privacy dataset. It achieves the following results on the evaluation set:

• Loss: 0.3681
• Accuracy: 0.9053
• F1: 0.8088

Model description

Model predicts whether to mask names as pseudonyms in any text. Input format should be a paragraph with names masked. It will then output whether to use a pseudonym because the EOIR courts would not allow such private/sensitive information to become public unmasked.

Intended uses & limitations

This is a minimal privacy standard and will likely not work on out-of-distribution data.

Training and evaluation data

We train on the EOIR Privacy dataset and evaluate further using sensitivity analyses.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 5

Training results

Framework versions

• Transformers 4.18.0
• Pytorch 1.11.0+cu113
• Datasets 2.1.0
• Tokenizers 0.12.1

Citation

@misc{hendersonkrass2022pileoflaw,
  url = {https://arxiv.org/abs/2207.00220},
  author = {Henderson*, Peter and Krass*, Mark S. and Zheng, Lucia and Guha, Neel and Manning, Christopher D. and Jurafsky, Dan and Ho, Daniel E.},
  title = {Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset},
  publisher = {arXiv},
  year = {2022}
}