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---
license: apache-2.0
datasets:
- grammarly/pseudonymization-data
language:
- en
metrics:
- f1
- bleu
pipeline_tag: text2text-generation
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This repository contains files for two Seq2Seq transformers-based models used in our paper: https://arxiv.org/abs/2306.05561.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **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://arxiv.org/abs/2306.05561
## 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*10<sup>5</sup>, and the batch size is 8.
## Evaluation
### Factors & Metrics
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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 F<sub>1</sub> score based on the abovementioned values.
## Citation
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
```
@misc{yermilov2023privacy,
title={Privacy- and Utility-Preserving NLP with Anonymized Data: A case study of Pseudonymization},
author={Oleksandr Yermilov and Vipul Raheja and Artem Chernodub},
year={2023},
eprint={2306.05561},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## Model Card Contact
Oleksandr Yermilov ([email protected]). |