README.md

---
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
```python
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`](https://huggingface.co/datasets/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.