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README.md

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+---
+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
+- text
+language:
+- en
+size_categories:
+- 100K<n<1M
+---
+
+# paperlens-text
+
+Text version of the **OpenReview-ICLR** and **arXiv** PaperLens datasets.
+
+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). [`reconstruction.py`](https://github.com/zlab-princeton/PaperLens/blob/main/paperlens-training-and-inference/scripts/reconstruction.py) reads this field to materialize the original sharegpt `data.json` for any of the ~20 publishable text 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("skonan/paperlens-text", "arxiv",           split="papers")
+ds_iclr  = load_dataset("skonan/paperlens-text", "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 (the full paper body in markdown) |
+| `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 |
+
+## Reconstructing the sharegpt `data.json` files
+
+[`reconstruction.py`](https://github.com/zlab-princeton/PaperLens/blob/main/paperlens-training-and-inference/scripts/reconstruction.py) rebuilds any of the publishable internal keys (e.g. `arxiv_50_50_21k_text_..._y24up_test`) byte-identically from this dataset. Setup + run:
+
+```bash
+git clone https://github.com/zlab-princeton/PaperLens.git
+cd PaperLens/paperlens-training-and-inference
+uv sync
+
+# arxiv training set
+uv run python scripts/reconstruction.py \
+    --hf_text_repo skonan/paperlens-text \
+    --dataset_keys arxiv_50_50_balanced_per_venue_text_wmetadata_filtered24480_train
+
+# openreview-iclr training set
+uv run python scripts/reconstruction.py \
+    --hf_text_repo skonan/paperlens-text \
+    --dataset_keys iclr_2020_2023_2025_2026_85_5_10_balanced_original_text_labelfix_v7_filtered_train
+```
+
+Reconstructed files land in `./data/` by default (override with `--data_root <path>`): `data/<dataset_key>/data.json` (sharegpt rows) and `data/dataset_info.json` (LlamaFactory entry).
+
+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).
+
+## License & citation
+
+License: see the [PaperLens collection](https://huggingface.co/collections/skonan/paperlens-6a0c79da423c3a436b7f6b1a).
+
+```bibtex
+@misc{konan2026paperlens,
+  title       = {PaperLens: How Predictable Is Paper Acceptance?},
+  author      = {Konan, Sachin and Liu, Jonathan and Liu, Zhuang},
+  year        = {2026},
+  institution = {Princeton University}
+}
+```

arxiv/data-shard-0000.parquet

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iclr/data-shard-0000.parquet

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