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
Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
---|---|---|---|---|---|
No log | 1.0 | 395 | 0.3053 | 0.8789 | 0.7432 |
0.3562 | 2.0 | 790 | 0.2857 | 0.8976 | 0.7883 |
0.2217 | 3.0 | 1185 | 0.3358 | 0.8905 | 0.7550 |
0.1509 | 4.0 | 1580 | 0.3505 | 0.9040 | 0.8077 |
0.1509 | 5.0 | 1975 | 0.3681 | 0.9053 | 0.8088 |
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}
}
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Evaluation results
- Accuracy on eoir_privacyself-reported0.905
- F1 on eoir_privacyself-reported0.809