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---
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}
}
```
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