| --- |
| license: other |
| configs: |
| - config_name: arxiv |
| data_files: |
| - split: papers |
| path: arxiv/*.parquet |
| - config_name: openreview-iclr |
| data_files: |
| - split: papers |
| path: iclr/*.parquet |
| tags: |
| - academic-paper-review |
| - paper-review |
| - sharegpt |
| - vision |
| language: |
| - en |
| size_categories: |
| - 100K<n<1M |
| --- |
| |
| # pl-vision-nips |
|
|
| Vision version of the **OpenReview-ICLR** and **arXiv** PaperLens datasets. |
| Anonymous release for double-blind review. |
|
|
| Each row is one unique paper. We release **all** extracted papers — not every |
| paper here is used in our downstream training/eval sets. The papers that *are* |
| used are denoted by the `references` field, which lists every internal |
| `(release_name, release_split)` pair the paper belongs to (a single paper can |
| belong to multiple). The accompanying `reconstruction.py` in the anonymous code |
| release reads this field to materialize the original sharegpt `data.json` for |
| any of the publishable vision keys. |
|
|
| ## Configs (subsets) |
|
|
| - `arxiv` — papers from arXiv (per_venue + 21k families + residual + the arXiv side of combined). |
| - `openreview-iclr` — papers from ICLR via OpenReview (balanced_original + max_rejects + train_50pct/75pct + the iclr side of combined). |
|
|
| ```python |
| from datasets import load_dataset |
| ds_arxiv = load_dataset("anonuser231357/pl-vision-nips", "arxiv", split="papers") |
| ds_iclr = load_dataset("anonuser231357/pl-vision-nips", "openreview-iclr", split="papers") |
| ``` |
|
|
| ## Schema |
|
|
| | field | type | description | |
| |---|---|---| |
| | `paper_id` | `string` | arXiv id or OpenReview submission id | |
| | `title` | `string` | paper title | |
| | `content` | `string` | prompt-stripped body (title + abstract + `<image>` placeholders (one per page)) | |
| | `metadata` | `string` | JSON blob — venue, year, authors, ratings, decision, … | |
| | `label` | `string` | `"Accept"` or `"Reject"` | |
| | `references` | `list<list<string>>` | each entry is `[release_name, release_split]` — the internal splits this paper belongs to | |
| | `images` | `list<struct<bytes,path>>` | one PNG per page, bytes inline | |
|
|
| ## Reconstructing the sharegpt `data.json` files |
|
|
| `reconstruction.py` (in the anonymous code release) rebuilds any of the |
| publishable internal keys (e.g. `arxiv_50_50_21k_vision_..._y24up_test`) |
| byte-identically from this dataset. Point `--hf_vision_repo` at this repo: |
|
|
| ```bash |
| python scripts/reconstruction.py \ |
| --hf_vision_repo anonuser231357/pl-vision-nips \ |
| --dataset_keys arxiv_50_50_balanced_per_venue_vision_wmetadata_filtered24480_train |
| ``` |
|
|
| Reconstructed files land in `./data/` by default (override with `--data_root <path>`): |
| `data/<dataset_key>/data.json` (sharegpt rows), `data/dataset_info.json` |
| (LlamaFactory entry), and `data/images_{arxiv,iclr}/<paper_id>/page_*.png` for |
| the per-page PNGs. |
|
|
| The release ships a `manifest.json` sidecar mapping each internal |
| `dataset_info.json` key → `(release_name, release_split, columns, file_name)`, |
| so reconstruction reproduces conversations, `_metadata`, |
| `accept_reject_label` (where applicable), and image bytes exactly. |
|
|
| > Reconstructing the full vision tree produces one PNG per page, creating over |
| > 1M files for the whole release — run on a fileset with inode headroom. |
|
|
| ## License & citation |
|
|
| License: other. Citation withheld for anonymous review. |
|
|
