---
title: olmOCR-synthmix-1025
canonical_url: "https://www.modelscope.cn/datasets/allenai/olmOCR-synthmix-1025"
md_url: "https://www.modelscope.cn/datasets/allenai/olmOCR-synthmix-1025.md"
repository: allenai/olmOCR-synthmix-1025
last_updated: 2025-11-26
license: odc-by
storage_size: "1.1 GB"
downloads: 27799
stars: 1
---

# olmOCR-synthmix-1025

> olmOCR-synthmix-1025 - allenai 在 ModelScope 开源的数据集。olmOCR-synthmix-1025

allenai/olmOCR-synthmix-1025 是 ModelScope 魔搭社区上的数据集，存储大小 1.1 GB，采用 odc-by 许可。

- **Repository**: allenai/olmOCR-synthmix-1025
- **License**: odc-by
- **Storage size**: 1.1 GB
- **Downloads**: 27799
- **Stars**: 1
- **Last updated**: 2025-11-26

Source: https://www.modelscope.cn/datasets/allenai/olmOCR-synthmix-1025

---

# olmOCR-synthmix-1025

olmOCR-synthmix-1025 is a dataset of 2,186 single PDF pages, that have been synthetically rerendered into HTML by
`claude-sonnet-4-20250514`.

In total, across these PDF pages, 30,381 synthetic benchmark cases have been created, following the format of [olmOCR-bench](https://huggingface.co/datasets/allenai/olmOCR-bench).

These documents contain no overlap with the original olmOCR-bench documents, and thus can be used as RLVR training
data to improve the performance of OCR engines.

## Directory Structure

```
olmocr-synthmix-1025/
  ├── bench_data/                    # olmOCR-bench format benchmark data
  │   ├── *.jsonl                    # olmOCR-bench test cases (5 files)
  │   │   ├── arxiv_cs.jsonl
  │   │   ├── arxiv_math.jsonl
  │   │   ├── arxiv_physics.jsonl
  │   │   ├── general.jsonl
  │   │   └── tables.jsonl
  │   ├── pdfs/                      # olmOCR-bench PDF files (2,187 files)
  │   │   ├── arxiv_cs/             (337 files)
  │   │   ├── arxiv_math/           (342 files)
  │   │   ├── arxiv_physics/        (102 files)
  │   │   ├── general/              (952 files)
  │   │   └── tables/               (454 files)
  │   └── claude_original/           # Original Claude OCR outputs (2,197 files)
  │       ├── arxiv_cs/             (337 files)
  │       ├── arxiv_math/           (342 files)
  │       ├── arxiv_physics/        (102 files)
  │       ├── general/              (956 files)
  │       └── tables/               (460 files)
  │
  ├── html/                          # HTML renders of each PDF page from Claude Sonnet(2,197 files)
  │   ├── arxiv_cs/                 (337 files)
  │   ├── arxiv_math/               (342 files)
  │   ├── arxiv_physics/            (102 files)
  │   ├── general/                  (956 files)
  │   └── tables/                   (460 files)
  │
  ├── metadata/                      # Additional metadata for each PDF, contains original URLS (5 files)
  │   ├── arxiv_cs.jsonl
  │   ├── arxiv_math.jsonl
  │   ├── arxiv_physics.jsonl
  │   ├── general.jsonl
  │   └── tables.jsonl
  │
  ├── pdfs/                          # Side-by-side Original and HTML-rerendered PDFs (4,394 files)
  │   ├── arxiv_cs/                 (674 files)
  │   ├── arxiv_math/               (684 files)
  │   ├── arxiv_physics/            (204 files)
  │   ├── general/                  (1,912 files)
  │   └── tables/                   (920 files)
  │
  └── training/                      # Training data with markdown + PDFs (4,394 files)
      ├── arxiv_cs/                 (674 files: .md + .pdf pairs)
      ├── arxiv_math/               (684 files: .md + .pdf pairs)
      ├── arxiv_physics/            (204 files: .md + .pdf pairs)
      ├── general/                  (1,912 files: .md + .pdf pairs)
      └── tables/                   (920 files: .md + .pdf pairs)
```

### How to use this dataset

1. You may test your own OCR model's performance on this, it is a perfectly valid olmOCR-bench style benchmark that can be run
using the standard olmOCR-bench tools located [here](https://github.com/allenai/olmocr/tree/main/olmocr/bench).

2. You may also choose to train your model using GRPO or similar techniques on this data. See the [olmOCR trainer code](https://github.com/allenai/olmocr/tree/main/olmocr/train) for more details.

### How this dataset was made

Please see the [mine_html_templates.py](https://github.com/allenai/olmocr/blob/main/olmocr/bench/synth/mine_html_templates.py) script in the olmOCR repo.
This script was run against 5 different subsets of PDFs.

`arxiv_cs`, `arxiv_math`, `arxiv_physics` each were samples of recent arxiv papers from those subsets.

`general` consists of files sampled from the same internal crawl of web-pdfs as olmOCR-mix-0225 uses.

`tables` consists of  files sampled from the same internal crawl of web-pdfs as olmOCR-mix-0225 uses, but filtered to pages that include a table using a script that prompts gpt-4o.

# License
This dataset is licensed under ODC-BY. It is intended for research and educational use in accordance with Ai2's [Responsible Use Guidelines](https://allenai.org/responsible-use).