README.md

metadata

language:
  - en
license: cc-by-4.0
size_categories:
  - 100M<n<1B
task_categories:
  - image-to-text
  - visual-question-answering
dataset_info:
  - config_name: CC-MAIN-2013-20
    features:
      - name: general_metadata
        struct:
          - name: domain
            sequence: string
          - name: fluency_prob
            dtype: float64
          - name: id
            dtype: string
          - name: non_advertisement_prob
            dtype: float64
          - name: politics_prob
            dtype: float64
          - name: porn_prob
            dtype: float64
          - name: toxic_prob
            dtype: float64
          - name: url
            dtype: string
      - name: images
        sequence: string
      - name: texts
        sequence: string
      - name: metadata
        list:
          - name: aesthetic_prob
            dtype: float64
          - name: bytes
            dtype: int64
          - name: d_hash
            dtype: string
          - name: d_hash_dup_count
            dtype: int64
          - name: height
            dtype: int64
          - name: img_url_sha
            dtype: string
          - name: p_hash
            dtype: string
          - name: p_hash_dup_count
            dtype: int64
          - name: unsafe_prob
            dtype: float64
          - name: width
            dtype: int64
    splits:
      - name: train
        num_bytes: 19908676196
        num_examples: 3878063
    download_size: 9303464923
    dataset_size: 19908676196
  - config_name: CC-MAIN-2013-48
    features:
      - name: general_metadata
        struct:
          - name: domain
            sequence: string
          - name: fluency_prob
            dtype: float64
          - name: id
            dtype: string
          - name: non_advertisement_prob
            dtype: float64
          - name: politics_prob
            dtype: float64
          - name: porn_prob
            dtype: float64
          - name: toxic_prob
            dtype: float64
          - name: url
            dtype: string
      - name: images
        sequence: string
      - name: texts
        sequence: string
      - name: metadata
        list:
          - name: aesthetic_prob
            dtype: float64
          - name: bytes
            dtype: int64
          - name: d_hash
            dtype: string
          - name: d_hash_dup_count
            dtype: int64
          - name: height
            dtype: int64
          - name: img_url_sha
            dtype: string
          - name: p_hash
            dtype: string
          - name: p_hash_dup_count
            dtype: int64
          - name: unsafe_prob
            dtype: float64
          - name: width
            dtype: int64
    splits:
      - name: train
        num_bytes: 15282078925
        num_examples: 3091537
    download_size: 6965036866
    dataset_size: 15282078925
  - config_name: CC-MAIN-2014-10
    features:
      - name: general_metadata
        struct:
          - name: domain
            sequence: string
          - name: fluency_prob
            dtype: float64
          - name: id
            dtype: string
          - name: non_advertisement_prob
            dtype: float64
          - name: politics_prob
            dtype: float64
          - name: porn_prob
            dtype: float64
          - name: toxic_prob
            dtype: float64
          - name: url
            dtype: string
      - name: images
        sequence: string
      - name: texts
        sequence: string
      - name: metadata
        list:
          - name: aesthetic_prob
            dtype: float64
          - name: bytes
            dtype: int64
          - name: d_hash
            dtype: string
          - name: d_hash_dup_count
            dtype: int64
          - name: height
            dtype: int64
          - name: img_url_sha
            dtype: string
          - name: p_hash
            dtype: string
          - name: p_hash_dup_count
            dtype: int64
          - name: unsafe_prob
            dtype: float64
          - name: width
            dtype: int64
    splits:
      - name: train
        num_bytes: 7227087609
        num_examples: 1390034
    download_size: 3259239561
    dataset_size: 7227087609
configs:
  - config_name: CC-MAIN-2013-20
    data_files:
      - split: train
        path: CC-MAIN-2013-20/train-*
  - config_name: CC-MAIN-2013-48
    data_files:
      - split: train
        path: CC-MAIN-2013-48/train-*
  - config_name: CC-MAIN-2014-10
    data_files:
      - split: train
        path: CC-MAIN-2014-10/train-*

We are uploading the dataset files ~

OmniCorpus-CC

This is the repository of OmniCorpus-CC, which contains 988 million image-text interleaved documents collected from Common Crawl.

• Repository: https://github.com/OpenGVLab/OmniCorpus
• Paper: https://arxiv.org/abs/2406.08418

OmniCorpus dataset is a large-scale image-text interleaved dataset, which pushes the boundaries of scale and diversity by encompassing 8.6 billion images interleaved with 1,696 text tokens from diverse sources, significantly surpassing previous datasets. This dataset demonstrates several advantages over its counterparts:

1. Larger data scale: Our dataset is 1.7 times larger in images and 12.5 times larger in texts compared to the previously largest multimodal dataset, LAION-5B, while maintaining excellent data quality.
2. Richer data diversity: Drawing from a broader range of data sources, our dataset is more diverse than other image-text interleaved datasets. It includes bilingual multimodal data in both Chinese and English, and encompasses text-centric and vision-centric documents extracted from common websites and video platforms.
3. More flexible format: The streaming data format of our dataset offers exceptional flexibility, allowing adaptation to various data structures, including pure text corpora, image-text pairs, and interleaved data formats.

The OmniCorpus contains three sections:

• OmniCorpus-CC: processed from dumps in Common Crawl from 2013 to Nov./Dec. 2023.
• OmniCorpus-CW: sourced from Chinese internet resources, will be availiable in OpenDataLab platform.
• OmniCorpus-YT: samples Youtube video frames as images and collects subtitles as texts.

Code for pre-training, evaluating, main body extracting, and filtering have been released in the official repository. A pre-trained model is availiable here. We are processing and uploading the rest data sections as soon as possible.

Update (2024-10-16):

We are uploading the natural arrangement version of the OmniCorpus-CC documents.

Coming soon:

• Documents with Similarities: Documents with split at the sentence level, resulting in minor differences of text content.

Data Pipeline

Our data pipeline consists of five key stages: main body extraction, preliminary text filtering, document deduplication, image downloading & filtering, and detailed text filtering. Each stage efficiently reduces the dataset to retain only high-quality data. Please refer to our paper for more details about the data pipeline.

Usages

The image-text interleaved documents are recommanded for the following usages:

• Pre-training multimodal large language model (MLLM): Recent MLLMs (such as Flamingo series, EMU series, IDEFICS series, MM1, Cambrian-1, and xGen-MM) have shown that image-text interleaved data aids multimodal in-context learning and maintains the capabilities of large language models during multimodal fine-tuning.
• Long text-image retrieval: We provide image-text similarities calculated with CLIP, which can convert the documents to image-text retrieval dataset with longer text. A retrieval model pre-trained on such data can retrieval images based on longer text, which can be used for multimodal RAG, converting pure text to multimodal sample, etc.
• Source for futher dataset research: Our data is large-scale, which can serve as the source for researches for data curation strategies. We provide many useful attributes as metadata for each document, which can enrich the filtering strategy and reduce the cost.
• ......

Data Format

Following common practices, the data is organized into Parquet file format. You might encounter errors when using pandas.read_parquet (because the data structure contains nested elements). We recommend using fastparquet to load the parquet files.

import fastparquet
df = fastparquet.ParquetFile(parquet_file_path).to_pandas()

# You can also use iter_batches
parquet_file = pq.ParquetFile(filepath)
for batch in parquet_file.iter_batches():
    df = batch.to_pandas()

You can convert the i-th document and convert it into a dictionary.

doc_dict = df.iloc[i].to_dict()

The document format is as follow:

{
    'images': [
        <str: image_1_url>,
        None,
        <str: image_2_url>,
        None,
    ],
    'texts': [
        None,
        <str: text_paragraph_1_content>
        None,
        <str: text_paragraph_2_content>,
    ]
    'metadata': [
        <dict: image_1_metadata>,
        None,
        <dict: image_2_metadata>,
        None
    ],
    'general_metadata': {
        "url": <str: document url>,
        "id": <str: document id>,
        "domain": <list[str]: domains extracted from document url>,
        "fluency_prob": <float: the probability of fluency>,
        "non_advertisement_prob": <float: the probability of non-advertisement>,
        "porn_prob": <float: the probability of porn content>,
        "politics_prob": <float: the probability of politics content>,
        "toxic_prob": <float: the probability of toxic content>,
    }
}

Each image metadata is as follow:

{
    "img_url_sha": <str: sha code of image url>,
    "width": <int: image width>,
    "height": <int: image height>,
    "bytes": <int: byte number of the image file>,
    "d_hash": <str: d_hash code of the image, used for image deduplication>,
    "p_hash": <str: p_hash code of the image, used for image deduplication>,
    "d_hash_dup_count": <int: duplicated times detected by d_hash code>,
    "p_hash_dup_count": <int: duplicated times detected by p_hash code>,
    "aesthetic prob": <float: aesthetic probility>,
    "unsafe prob": <float: NSFW probility>, 
}

License

OmniCorpus is released under a CC-BY-4.0 license, with the primary intent of supporting research activities.

Citation

@article{li2024omnicorpus,
  title={OmniCorpus: A Unified Multimodal Corpus of 10 Billion-Level Images Interleaved with Text},
  author={Li, Qingyun and Chen, Zhe and Wang, Weiyun and Wang, Wenhai and Ye, Shenglong and Jin, Zhenjiang and others},
  journal={arXiv preprint arXiv:2406.08418},
  year={2024}
}