data: refresh 1 partition(s) (backfill)

README.md

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+---
+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).

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