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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- eoir_privacy |
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metrics: |
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- accuracy |
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- f1 |
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model-index: |
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- name: distilbert-base-uncased-finetuned-eoir_privacy |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: eoir_privacy |
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type: eoir_privacy |
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args: all |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9052835051546392 |
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- name: F1 |
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type: f1 |
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value: 0.8088426527958388 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# distilbert-base-uncased-finetuned-eoir_privacy |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the eoir_privacy dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3681 |
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- Accuracy: 0.9053 |
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- F1: 0.8088 |
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## Model description |
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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. |
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## Intended uses & limitations |
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This is a minimal privacy standard and will likely not work on out-of-distribution data. |
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## Training and evaluation data |
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We train on the EOIR Privacy dataset and evaluate further using sensitivity analyses. |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| |
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| No log | 1.0 | 395 | 0.3053 | 0.8789 | 0.7432 | |
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| 0.3562 | 2.0 | 790 | 0.2857 | 0.8976 | 0.7883 | |
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| 0.2217 | 3.0 | 1185 | 0.3358 | 0.8905 | 0.7550 | |
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| 0.1509 | 4.0 | 1580 | 0.3505 | 0.9040 | 0.8077 | |
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| 0.1509 | 5.0 | 1975 | 0.3681 | 0.9053 | 0.8088 | |
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### Framework versions |
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- Transformers 4.18.0 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.1.0 |
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- Tokenizers 0.12.1 |
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### Citation |
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``` |
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@misc{hendersonkrass2022pileoflaw, |
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url = {https://arxiv.org/abs/2207.00220}, |
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author = {Henderson*, Peter and Krass*, Mark S. and Zheng, Lucia and Guha, Neel and Manning, Christopher D. and Jurafsky, Dan and Ho, Daniel E.}, |
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title = {Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset}, |
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publisher = {arXiv}, |
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year = {2022}, |
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copyright = {arXiv.org perpetual, non-exclusive license} |
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} |
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``` |
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