---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- eoir_privacy
metrics:
- accuracy
- f1
base_model: distilbert-base-uncased
model-index:
- name: distilbert-base-uncased-finetuned-eoir_privacy
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: eoir_privacy
      type: eoir_privacy
      args: all
    metrics:
    - type: accuracy
      value: 0.9052835051546392
      name: Accuracy
    - type: f1
      value: 0.8088426527958388
      name: F1
---

<!-- 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}
}
```