README.md

---
pretty_name: "ArXivSignals FullText — arXiv Papers OCR'd to Markdown + Layout"
license: other
license_name: mixed-see-description
language:
  - en
tags:
  - arxiv
  - scientific-papers
  - ocr
  - full-text
  - document-understanding
  - markdown
  - layout-analysis
task_categories:
  - text-generation
  - text-retrieval
  - feature-extraction
size_categories:
  - 10K<n<100K
configs:
  - config_name: papers
    default: true
    data_files:
      - split: corpus
        path: data/papers/**/*.parquet
---

# ArXivSignals FullText — arXiv Papers OCR'd to Markdown + Layout

A continuously-updated, day-partitioned dataset of arXiv papers **converted to
clean full text** by a vision OCR pipeline: each paper's PDF is rendered to
Markdown (headings, paragraphs, tables as HTML, math as LaTeX) plus a structured
**layout JSON** (typed, bounding-boxed blocks). It is the full-text companion to
[`taesiri/ArXivSignals`](https://huggingface.co/datasets/taesiri/ArXivSignals)
(metadata + LLM signal & summaries) and joins it on `paper_id`.

## How it's made

- **Source:** arXiv PDFs (publicly available).
- **OCR:** a document vision-language model (Baidu-family "Unlimited-OCR" via MLX),
  run at 4-bit precision, page-by-page, producing Markdown + a raw tagged form + a
  typed layout tree. No LLM rewriting — this is transcription, not summarization.
- **Scope:** papers that are also in the public [ArXivSignals](https://arxivsignals.io)
  catalog. The corpus fills in continuously (newest-first); coverage grows daily.

## Schema (`papers` config, partitioned by `announce_date`)

| Column | Type | Notes |
|---|---|---|
| `paper_id` | string | arXiv id (joins `taesiri/ArXivSignals`) |
| `announce_date` | date | arXiv announcement date (partition key) |
| `title`, `abstract` | string | arXiv metadata |
| `author_names` | list<string> | display names |
| `authors_json` | string | full author structure (JSON) |
| `categories` | list<string> | arXiv categories |
| `primary_category` | string | |
| **`ocr_markdown`** | string | **the OCR'd full text** (Markdown; HTML tables; LaTeX math) |
| **`ocr_layout`** | string | typed + bbox'd layout blocks (JSON: `{items:[{page,type,bbox,content}]}`) |
| `pages` | int | page count |
| `md_chars`, `n_layout_blocks` | int | content stats |
| `ocr_tokens`, `ocr_tps`, `ocr_duration_s` | numeric | OCR throughput stats |
| `ocr_precision`, `ocr_model` | string | OCR configuration |
| `ocr_finished_at` | string | when this paper was OCR'd |

```python
from datasets import load_dataset
ds = load_dataset("taesiri/ArXivSignals-FullText", "papers", split="corpus")
print(ds[0]["ocr_markdown"][:500])
```

## Licensing, attribution & takedown

**This is important — read before redistributing.** The `ocr_markdown` /
`ocr_layout` fields are a machine-generated transcription of arXiv PDFs. **arXiv
papers are licensed individually by their authors** (arXiv's default non-exclusive
license, or CC-BY / CC-BY-SA / CC0 / other, per submission), and **that license
governs the underlying content of the OCR text.** This dataset does **not** grant
any rights beyond those of each source paper.

- **Metadata** (title, abstract, authors, categories) is provided under
  [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/), consistent with the
  companion catalog dataset.
- **OCR full text** is provided **for research and text/data-mining purposes**, as a
  derived representation of publicly available papers. Redistribution or reuse of any
  paper's text is subject to that paper's own license — check it before reusing.
- **Attribution:** always cite the original arXiv paper (`paper_id`), not this dataset,
  as the source of the content.
- **OCR is imperfect:** expect errors in math, tables, multi-column, and scanned pages.
  Treat the text as machine-transcribed, not authoritative.
- **Takedown / opt-out:** if you are an author (or rights holder) and want a paper
  removed, open an issue / discussion on this dataset repo — it will be removed promptly.

Maintained by [@taesiri](https://huggingface.co/taesiri) · powers
[arxivsignals.io](https://arxivsignals.io).