Upload README.md with huggingface_hub

README.md

@@ -1,36 +1,47 @@
 ---
-dataset_info:
-  features:
-  - name: paper_id
-    dtype: string
-  - name: source
-    dtype: string
-  - name: year
-    dtype: int32
-  - name: paper_text
-    dtype: string
-  - name: anonymized_paper_text
-    dtype: string
-  - name: decision_label
-    dtype: string
-  - name: decision_text
-    dtype: string
-  - name: average_review_score
-    dtype: float32
-  splits:
-  - name: train
-    num_bytes: 314519513
-    num_examples: 1885
-  - name: test
-    num_bytes: 78588165
-    num_examples: 471
-  download_size: 186811222
-  dataset_size: 393107678
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
+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.