Datasets:

Modalities:
Text
Formats:
parquet
Languages:
English
ArXiv:
Libraries:
Datasets
Dask
License:
File size: 9,351 Bytes
aea48e3
4c0dba4
 
 
 
 
1f41654
4c0dba4
 
 
 
 
8927af0
4c0dba4
 
8927af0
aea48e3
4c0dba4
aea48e3
 
 
 
 
 
 
 
 
 
 
 
 
 
 
adf875c
4c0dba4
 
 
 
 
 
 
 
 
 
 
 
 
 
 
aea48e3
1f41654
aea48e3
66a024b
 
08b7dca
 
031520b
66a024b
 
f616da2
66a024b
9edf840
66a024b
a73938c
4834ad1
0527ed0
 
66a024b
ce6e440
 
f616da2
9edf840
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
f742802
f616da2
f742802
f616da2
f742802
f616da2
f742802
9edf840
6f833eb
9edf840
6f833eb
9edf840
6f833eb
f616da2
6f833eb
5edc60f
 
 
 
 
 
012b642
5edc60f
 
 
 
 
 
 
 
57f86f5
d78f985
5edc60f
 
9edf840
a0287b7
9edf840
a0287b7
6aa153a
aff6f31
9edf840
aff6f31
9edf840
a0287b7
6aa153a
a0287b7
2945aab
66a024b
2945aab
66a024b
 
 
2945aab
66a024b
 
 
 
1004b72
1f41654
 
9edf840
 
 
 
 
1004b72
9edf840
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
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
---
language:
- en
license: cc-by-4.0
size_categories:
- 100M<n<1B
pretty_name: OBELICS
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
- config_name: opt_out_docs_removed_2023_07_12
  data_files:
  - split: train
    path: opt_out_docs_removed_2023_07_12/train-*
dataset_info:
- config_name: default
  features:
  - name: images
    sequence: string
  - name: metadata
    dtype: string
  - name: general_metadata
    dtype: string
  - name: texts
    sequence: string
  splits:
  - name: train
    num_bytes: 715724717192
    num_examples: 141047697
  download_size: 71520629655
  dataset_size: 715724717192
- config_name: opt_out_docs_removed_2023_07_12
  features:
  - name: images
    sequence: string
  - name: metadata
    dtype: string
  - name: general_metadata
    dtype: string
  - name: texts
    sequence: string
  splits:
  - name: train
    num_bytes: 684638314215
    num_examples: 134648855
  download_size: 266501092920
  dataset_size: 684638314215
---
# Dataset Card for OBELICS

## Dataset Description

- **Visualization of OBELICS web documents:** https://huggingface.co/spaces/HuggingFaceM4/obelics_visualization
- **Paper:** [OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents](https://arxiv.org/abs/2306.16527)
- **Repository:** https://github.com/huggingface/OBELICS
- **Point of Contact: hugo@huggingface.co**

`OBELICS` is an open, massive, and curated collection of interleaved image-text web documents, containing 141M English documents, 115B text tokens, and 353M images, extracted from Common Crawl dumps between February 2020 and February 2023. The collection and filtering steps are described in our [paper](https://huggingface.co/papers/2306.16527).

Interleaved image-text web documents are a succession of text paragraphs interleaved by images, such as web pages that contain images. Models trained on these web documents outperform vision and language models trained solely on image-text pairs on various benchmarks. They can also generate long and coherent text about a set of multiple images. As an example, we trained [IDEFICS](https://huggingface.co/HuggingFaceM4/idefics-80b), a visual language model that accepts arbitrary sequences of image and text inputs and produces text outputs.

We provide an [interactive visualization](https://atlas.nomic.ai/map/f2fba2aa-3647-4f49-a0f3-9347daeee499/ee4a84bd-f125-4bcc-a683-1b4e231cb10f) of OBELICS that allows exploring the content of OBELICS. The map shows a subset of 11M of the 141M documents.

[![OBELICS Nomic map](assets/nomic_map.png)](https://atlas.nomic.ai/map/f2fba2aa-3647-4f49-a0f3-9347daeee499/ee4a84bd-f125-4bcc-a683-1b4e231cb10f)


## Data Fields

An example of a sample looks as follows:
```
# The example has been cropped

{
    'images': [
        'https://cdn.motor1.com/images/mgl/oRKO0/s1/lamborghini-urus-original-carbon-fiber-accessories.jpg',
        None
    ],
    'metadata': '[{"document_url": "https://lamborghinichat.com/forum/news/vw-group-allegedly-receives-offer-to-sell-lamborghini-for-9-2-billion.728/", "unformatted_src": "https://cdn.motor1.com/images/mgl/oRKO0/s1/lamborghini-urus-original-carbon-fiber-accessories.jpg", "src": "https://cdn.motor1.com/images/mgl/oRKO0/s1/lamborghini-urus-original-carbon-fiber-accessories.jpg", "formatted_filename": "lamborghini urus original carbon fiber accessories", "alt_text": "VW Group Allegedly Receives Offer To Sell Lamborghini For $9.2 Billion", "original_width": 1920, "original_height": 1080, "format": "jpeg"}, null]',
    'general_metadata': '{"url": "https://lamborghinichat.com/forum/news/vw-group-allegedly-receives-offer-to-sell-lamborghini-for-9-2-billion.728/", "warc_filename": "crawl-data/CC-MAIN-2021-25/segments/1623488528979.69/warc/CC-MAIN-20210623011557-20210623041557-00312.warc.gz", "warc_record_offset": 322560850, "warc_record_length": 17143}',
    'texts': [
        None,
        'The buyer would get everything, including Lambo\'s headquarters.\n\nThe investment groupQuantum Group AG has submitted a€7.5 billion ($9.2 billion at current exchange rates) offer to purchase Lamborghini from Volkswagen Group, Autocar reports. There\'s no info yet about whether VW intends to accept the offer or further negotiate the deal.\n\nQuantum ... Group Chief Executive Herbert Diess said at the time.'
    ]
}
```

Each sample is composed of the same 4 fields: `images`, `texts`, `metadata`, and `general_metadata`. `images` and `texts` are two lists of the same size, where for each index, one element and only one is not `None`. For example, for the interleaved web document `<image_1>text<image_2>`, we would find `[image_1, None, image_2]` in `images` and `[None, text, None]` in `texts`.

The images are replaced by their URLs, and the users need to download the images, for instance, with the library [img2dataset](https://github.com/rom1504/img2dataset).

`metadata` is the string representation of a list containing information about each of the images. It has the same length as `texts` and `images` and logs for each image relevant information such as original source document, unformatted source, alternative text if present, etc.

`general_metadata` is the string representation of a dictionary containing the URL of the document, and information regarding the extraction from Common Crawl snapshots.

## Size and Data Splits

There is only one split, `train`, that contains 141,047,697 documents.

`OBELICS` with images replaced by their URLs weighs 666.6 GB (😈) in arrow format and 377 GB in the uploaded `parquet` format.


## Considerations for Using the Data


### Discussion of Biases

A subset of this dataset `train`, of ~50k was evaluated using the Data Measurements Tool, with a particular focus on the nPMI metric 
> nPMI scores for a word help to identify potentially problematic associations, ranked by how close the association is.
> nPMI bias scores for paired words help to identify how word associations are skewed between the selected selected words (Aka et al., 2021).
> You can select from gender and sexual orientation identity terms that appear in the dataset at least 10 times.
> The resulting ranked words are those that co-occur with both identity terms.
> The more positive the score, the more associated the word is with the first identity term. The more negative the score, the more associated the word is with the second identity term.

While there was a positive skew of words relating occupations e.g _`government`_, _`jobs`_ towards she, her, and similar attributions of the masculine and feminine words to they and them, more harmful words attributions such as _`escort`_ and even _`colour`_ presented with greater attributions to she, her and him, his, respectively.

![Data Measurement Tool Associations Eval](assets/DMT_eval.png)

We welcome users to explore the [Data Measurements nPMI Visualitons for OBELICS](https://huggingface.co/spaces/HuggingFaceM4/IDEFICS_Data_Measurement_Tool) further and to see the [idefics-9b model card](https://huggingface.co/HuggingFaceM4/idefics-9b) for further Bias considerations.

## Opted-out content

To respect the preferences of content creators, we removed from OBELICS all images for which creators explicitly opted out of AI model training. We used the [Spawning API](https://api.spawning.ai/spawning-api) to verify that the images in the dataset respect the original copyright owners’ choices.

However, due to an error on our side, we did not remove entire documents (i.e., URLs) that opted out of AI model training. As of July 12, 2023, it represents 4.25% of the totality of OBELICS. The config `opt_out_docs_removed_2023_07_12` applies the correct filtering at the web document level as of July 2023: `ds = load_dataset("HuggingFaceM4/OBELICS", "opt_out_docs_removed_2023_07_12")`.

We recommend users of OBELICS to regularly check every document against the API.

## Content warnings

Despite our efforts in filtering, OBELICS contains a small proportion of documents that are not suitable for all audiences. For instance, while navigating the interactive map, you might find the cluster named "Sex" which predominantly contains descriptions of pornographic movies along with pornographic images. Other clusters would contain advertising for sex workers or reports of violent shootings. In our experience, these documents represent a small proportion of all the documents.

## Terms of Use

By using the dataset, you agree to comply with the original licenses of the source content as well as the dataset license (CC-BY-4.0). Additionally, if you use this dataset to train a Machine Learning model, you agree to disclose your use of the dataset when releasing the model or an ML application using the model.

### Licensing Information

License CC-BY-4.0.

### Citation Information

If you are using this dataset, please cite
```
@misc{laurencon2023obelics,
      title={OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents},
      author={Hugo Laurençon and Lucile Saulnier and Léo Tronchon and Stas Bekman and Amanpreet Singh and Anton Lozhkov and Thomas Wang and Siddharth Karamcheti and Alexander M. Rush and Douwe Kiela and Matthieu Cord and Victor Sanh},
      year={2023},
      eprint={2306.16527},
      archivePrefix={arXiv},
      primaryClass={cs.IR}
}
```