README.md

metadata

tags:
  - ocr
  - peer-review
  - classification
license: other
language:
  - en

Popper Reviews — Private Prediction Subset

Dataset Summary

This repository exposes an 80/20 train/test split tailored for acceptance prediction tasks. Each example contains:

• paper_text: OCR’d manuscript text.
• anonymized_paper_text: the same text with the author block removed (starts at the abstract).
• decision_label: normalized accept/reject outcome.
• decision_text: original decision string when available.
• average_review_score: mean of numeric reviewer ratings extracted from the Popper review JSON files.

Source corpora: Popper’s ICLR, TMLR, and Nature review dumps. Only papers with an explicit accept/reject decision are included. Reference lists are removed from anonymized_paper_text to focus on the manuscript narrative.

Splits

Split	Records
train	1 884
test	472

Splits are stratified with an 80/20 ratio using a fixed random seed (42).

Usage

from datasets import load_dataset

data = load_dataset("popper-spiralworks/prediction_task", split="train", token=token)
print(data[0]["decision_label"], data[0]["average_review_score"])

Processing Notes

• OCR text comes from DeepSeek-OCR via Popper (metadata.backend = deepseek when available).
• Average scores are computed by parsing the numeric prefix of each reviewer rating field.
• Non-numeric or missing ratings are ignored during averaging.
• Additional review metadata and reviewer comments are available in the public dataset sumuks/research_papers_with_reviews_ocr.

Attribution

When using this dataset, please credit the original venues (ICLR, TMLR, Nature) and cite the Popper project. Access to this repository is restricted to the Popper Spiralworks collaboration.