README.md

metadata

license: apache-2.0
datasets:
  - grammarly/pseudonymization-data
  - cnn_dailymail
  - imdb
language:
  - en
metrics:
  - f1
  - bleu
pipeline_tag: text2text-generation

Model Card for Model ID

This repository contains files for two Seq2Seq transformers-based models used in our paper: https://aclanthology.org/2023.trustnlp-1.20/.

Model Details

Model Description

• Developed by: Oleksandr Yermilov, Vipul Raheja, Artem Chernodub
• Model type: Seq2Seq
• Language (NLP): English
• License: Apache license 2.0
• Finetuned from model: BART

Model Sources

• Paper: https://aclanthology.org/2023.trustnlp-1.20/

Uses

These models can be used for anonymizing datasets in English language.

Bias, Risks, and Limitations

Please check the Limitations section in our paper.

Training Details

Training Data

https://huggingface.co./datasets/grammarly/pseudonymization-data/tree/main/seq2seq

Training Procedure

1. Gather text data from Wikipedia.
2. Preprocess it using NER-based pseudonymization.
3. Fine-tune BART model on translation task for translating text from "original" to "pseudonymized".

Training Hyperparameters

We train the models for 3 epochs using AdamW optimization with the learning rate α =2*10⁵, and the batch size is 8.

Evaluation

Factors & Metrics

Factors

There is no source truth of named entities for the data, on which this model was trained. We check whether the word is a named entity, using one of the NER systems (spaCy or FLAIR).

Metrics

We measure the amount of text, changed by our model. Specifically, we check for the following categories of translated text word by word:

1. True positive (TP) - Named entity, which was changed to another named entity.
2. True negative (TN) - Not a named entity, which was not changed.
3. False positive (FP) - Not a named entity, which was changed to another word.
4. False negative (FN) - Named entity, which was not changed to another named entity. We calculate F₁ score based on the abovementioned values.

Citation

BibTeX:

@inproceedings{yermilov-etal-2023-privacy,
    title = "Privacy- and Utility-Preserving {NLP} with Anonymized data: A case study of Pseudonymization",
    author = "Yermilov, Oleksandr  and
      Raheja, Vipul  and
      Chernodub, Artem",
    booktitle = "Proceedings of the 3rd Workshop on Trustworthy Natural Language Processing (TrustNLP 2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.trustnlp-1.20",
    doi = "10.18653/v1/2023.trustnlp-1.20",
    pages = "232--241",
    abstract = "This work investigates the effectiveness of different pseudonymization techniques, ranging from rule-based substitutions to using pre-trained Large Language Models (LLMs), on a variety of datasets and models used for two widely used NLP tasks: text classification and summarization. Our work provides crucial insights into the gaps between original and anonymized data (focusing on the pseudonymization technique) and model quality and fosters future research into higher-quality anonymization techniques better to balance the trade-offs between data protection and utility preservation. We make our code, pseudonymized datasets, and downstream models publicly available.",
}

Model Card Contact

Oleksandr Yermilov ([email protected]).