readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - eoir_privacy
  metrics:
  - accuracy
  - f1
  model-index:
  - name: distilbert-base-uncased-finetuned-eoir_privacy
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: eoir_privacy
        type: eoir_privacy
        args: all
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9052835051546392
      - name: F1
        type: f1
        value: 0.8088426527958388
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # distilbert-base-uncased-finetuned-eoir_privacy
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/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}
  }
  ```
  