File size: 3,289 Bytes
cde0654 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 | ---
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
---
# paperlens-vision
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/paperlens-vision", "arxiv", split="papers")
ds_iclr = load_dataset("anonuser231357/paperlens-vision", "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/paperlens-vision \
--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.
|