| --- |
| 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}, |
| } |
| ``` |
|
|
