File size: 6,338 Bytes
6be1cdd
86f8dde
6be1cdd
f603655
 
 
 
 
 
 
 
 
 
2569c9c
e1c5e0d
2569c9c
4c7ddea
f603655
 
 
b1d7dd4
 
f603655
4072bad
b81a539
7e3aa9c
d2c199e
bd13d3a
1027ac1
 
 
 
 
b7cfd33
1c731ff
1027ac1
e034440
 
 
 
 
 
390d3cf
 
2569c9c
 
e034440
2569c9c
3e06d64
 
 
2b709be
3e06d64
 
2569c9c
 
 
 
 
 
 
 
f603655
 
 
 
 
 
 
 
 
 
44f183b
2569c9c
 
 
 
 
a54413e
bec9f7e
 
e51081e
bec9f7e
 
 
 
 
 
a54413e
2569c9c
20f16db
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
---
license: cc-by-4.0
size_categories:
- '>1T'
task_categories:
- text-generation
language:
- multilingual
tags:
- common-crawl
- html-parsing
- web-corpus
- markdown
---
🔧 🔧  **Our New-Gen Html Parser [MinerU-HTML](https://github.com/opendatalab/MinerU-HTML)** Now Realease!

# AICC: AI-ready Common Crawl Dataset

[Paper](https://huggingface.co/papers/2511.16397) | [Project page](https://opendatalab.com/ai-ready/AICC)

<img src="./images/AICC_HF_LOGO.png" width="600" />

AICC (AI-ready Common Crawl) is a large-scale, **AI-ready web dataset** derived from **Common Crawl**, containing semantically extracted **Markdown-formatted** main content from diverse web pages. The dataset is constructed using the **MinerU-HTML**, a web extraction pipeline developed by OpenDataLab.
- **High-quality main content:** High-fidelity main content extracted from diverse Common Crawl pages, including challenging types like forums, Q&A sites, and pages with tables or formulas.
- **Precise structured elements:** High-fidelity extraction of code blocks, mathematical formulas, and complex tables from real-world web pages, preserving syntax, formatting, and structural integrity.
- **Proven downstream effectiveness:** Pretraining a language model on AICC leads to higher accuracy across diverse benchmarks compared to training on datasets extracted with other methods.
  
🎉🎉🎉 [Experience our online web extraction with your own HTML!!!](https://opendatalab.com/ai-ready/AICC)
## Dataset Creation
**Raw Html Source**  This release includes the parsed results from two **Common Crawl** dumps:
- CC-MAIN-2025-08
- CC-MAIN-2025-13

**MinerU-HTML Pipeline**  The detailed pipeline can be found in the [AICC technical report](https://arxiv.org/abs/2511.16397):
![](images/MinerU-HTML_core_extraction.jpg)
  
## Data Statistics
The AICC dataset contains only the successfully extracted AI-ready JSON records(each with a `content` field containing Markdown text).<br>
For reference, the number of original pages in the corresponding Common Crawl dumps is also shown below.<br>
Note that only the extracted JSON records are included in the released dataset.
| **Common Crawl Dump** | **AICC JSON records (lines)** | **Original pages (lines, not included)** |
|------------------------|------------------------------------|-----------------------------------------|
| CC-MAIN-2025-08 | 2,391,293,976 | 2,679,687,937 |
| CC-MAIN-2025-13 | 2,452,518,662 | 2,740,793,128 |  
## Data Format

| **Field name** | **Field description** | **Note** |
|------------|-------------------|------| 
| track\_id | Unique tracking identifier for the record |- | 
| url | Full original URL of the webpage, indicating the source of the content |- | 
| language | Primary language of the webpage | Identified using the fastText language detection model lid.176.bin | 
| content 🚩| Clean Markdown-formatted content extracted from the webpage HTML |- | 
| extract\_method | Name of the web content extraction method used |- | 
| sub\_path | Relative path or shard location within the original Common Crawl storage structure | Used to locate the record’s original source in WARC/WAT/WET files, supporting data traceability and verification|


**Data sample**
```json
  {
    "track_id": "5667aa9a-da8a-5c80-a678-d38609247cb5",
    "url": "https://cheezburger.com/14867205/dude-finds-giant-centipede-in-daughters-room-horrifies-people-with-the-footage",
    "language": "en",
    "content": "# Dude Finds Giant Centipede In Daughter's Room & Horrifies People With the Footage

Growing up in Brooklyn I was always terrified of house centipedes. And why wouldn't I be? The revoltingly speedy unibrow bugs were all over the place. They'd find their way onto my arm at slumber parties, inspiring blood-curdling screams more indicative of a murder than a creepy crawly. I'd find them in my shoes. My mother would tell me they're \"good bugs\" because they eat the \"bad bugs,\" but to me they were the stuff of literal nightmares. Eventually my fear subsided, after having to live in a dank and dark East Village basement where they were a daily sighting. I'd almost forgotten about the critters - until Twitter user@VickGlaze horrified the internet with a video that features a far more sinister-looking centipede.

I almost had set the crib on fire yesterday….Y'all wouldn't believe what I found in my daughters room dawg. I just spent a 50ball at Lowe's on everything pest control. I found where it came in and sealed every window in the crib and some. pic.twitter.com/iAoN7uWYsY

— Miami Vice II (@VickGlaze) July 27, 2021

The alarmingly large centipede in Vick's video is known by a few names. Some call them Texas redheaded centipedes, others the Giant Desert Centipede. Whatever its called, it's freaking terrifying. And this scared father was not afraid to admit it. In a thread, he details all the measures he took to protect his home, and the terror the creature has struck in his heart. The responses (mostly in solidarity) are almost as entertaining as the video and accompanying story. As for me? I'm feeling pretty damn lucky that I grew up in New York and not in Austin, Texas, where these massive venomous critters roam.
",
    "extract_method": "MinerU-HTML",
    "sub_path": "CC-MAIN-2025-08"
  }
```

## Citation
```
@misc{ma2025aiccparsehtmlfiner,
      title={AICC: Parse HTML Finer, Make Models Better -- A 7.3T AI-Ready Corpus Built by a Model-Based HTML Parser}, 
      author={Ren Ma and Jiantao Qiu and Chao Xu and Pei Chu and Kaiwen Liu and Pengli Ren and Yuan Qu and Jiahui Peng and Linfeng Hou and Mengjie Liu and Lindong Lu and Wenchang Ning and Jia Yu and Rui Min and Jin Shi and Haojiong Chen and Peng Zhang and Wenjian Zhang and Qian Jiang and Zengjie Hu and Guoqiang Yang and Zhenxiang Li and Fukai Shang and Runyuan Ma and Chenlin Su and Zhongying Tu and Wentao Zhang and Dahua Lin and Conghui He},
      year={2025},
      eprint={2511.16397},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2511.16397}, 
}
```

## License and Ethics
This dataset is licensed under **CC BY 4.0**, requiring attribution when used. It is derived from Common Crawl web pages and may contain biased or sensitive content; users are responsible for ethical and lawful usage in research or applications.