---
language:
- en
pretty_name: PostMark data files
tags:
- watermarking
- llm
---

# PostMark data files

Data files needed to run [PostMark](https://github.com/lilakk/PostMark), from the paper
[PostMark: A Robust Blackbox Watermark for Large Language Models](https://arxiv.org/abs/2406.14517) (EMNLP 2024).

These files were previously hosted on Google Drive. That link no longer works, so they are hosted here instead.

## Files

| File | Size | Used for |
|---|---|---|
| `paragram_xxl.pkl` | 2.0 GB | Paragram word embedding table (a pickled torch tensor). Used by `detect.py` to compute word presence scores, and loaded by every run. Its vocabulary is `paragram_xxl_words.json` in the GitHub repo. |
| `filtered_data_100k_unique_250w_sentbound_openai_embs.pkl` | 231 MB | OpenAI `text-embedding-3-large` (256-dim) document embeddings, used to build the word embedding table when running with `--embedder openai`. |
| `filtered_data_100k_unique_250w_sentbound_nomic_embs.pkl` | 335 MB | `nomic-embed-text-v1` document embeddings, used the same way when running with `--embedder nomic`. |

SHA-256 checksums are in `SHA256SUMS`.

## Usage

Download the files into the root of your PostMark checkout:

```bash
cd PostMark
hf download yapeichang/PostMark --repo-type dataset --local-dir . \
    --include "*.pkl" "SHA256SUMS"
sha256sum -c SHA256SUMS   # optional: verify the downloads
```

See the [GitHub README](https://github.com/lilakk/PostMark) for how to run watermarking and detection.

## Citation

```bibtex
@misc{chang2024postmark,
      title={PostMark: A Robust Blackbox Watermark for Large Language Models},
      author={Yapei Chang and Kalpesh Krishna and Amir Houmansadr and John Wieting and Mohit Iyyer},
      year={2024},
      eprint={2406.14517},
      archivePrefix={arXiv},
}
```