updates

README.md

@@ -56,49 +56,57 @@ dataset_info:
 - **Paper: OBELISC: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents**
 - **Point of Contact: hugo@huggingface.co**
 
-### Dataset Summary
+`OBELISC` 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. The collection and filtering steps are described in our [paper](https://huggingface.co/papers/2306.16527).
 
-`OBELISC` is an open, massive and curated collection of interleaved image-text web documents, containing 141M documents, 115B text tokens and 353M images.
+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.
 
-This dataset can be used to train large multimodal models, significantly improving their reasoning abilities compared to models trained solely on image/text pairs. Please refer to our paper for further details about the construction of the dataset, quantitative and qualitative analyses of `OBELISC`, and experiments we conducted.
+We provide an [interactive visualization](TODO once we have final link public) of OBELICS that allows exploring the content of OBELICS. The map shows a subset of 11M of the 141M documents.
 
-### Languages
-
-English
+![OBELICS Nomic map](assets/nomic_map.png)
 
 ## Data Fields
 
-There are 4 fields: `images`, `texts`, `metadata` and `general_metadata`.
-
-For each example, the data in the columns `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 web document `<image_1>text<image_2>`, in `images`, we have `[image_1,None,image_2]` and in `texts` we have `[None,text,None]`.
-
-The images are replaced by their URLs, and the users have to download them themselves, for example with the library `img2dataset`.
+An example of 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.'
+    ]
+}
+```
 
-In `metadata`, there is a string that can be transformed into a list with `json.loads(example["metadata"])`. This list will have the same size as the lists of images and texts, and will have a dictionary for each index where there is an image, and a `None` value when there is a text. This dictionary will contain the metadata of the image (original source document, unformatted source, alt-text if present, ...).
+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`.
 
-Finally, in `general_metadata`, there is a string that can be transformed into a dictionary, containing the URL of the document, and information about its location in the Common Crawl data.
+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).
 
-## Data Splits
+`metadata` is the string representation of a list containing informations about each of the images. It has the same length as `texts` and `images` and logs for each the image relevant information such as original source document, unformatted source, alternative text if present, etc.
 
-There is only one split, `train`, that contains 141,047,697 examples.
+`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
+## Size and Data Splits
 
-`OBELISC` with images replaced by their URLs weighs 666.6 GB (unwanted!) in arrow format and 377 GB in this uploaded `parquet` format.
+There is only one split, `train`, that contains 141,047,697 documents.
 
-### Visualization of OBELISC documents
+`OBELISC` with images replaced by their URLs weights 666.6 GB (😈) in arrow format and 377 GB in the uploaded `parquet` format.
 
-https://huggingface.co/spaces/HuggingFaceM4/obelisc_visualization
+## Opted-out content
 
-### Research paper
+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.
 
-https://arxiv.org/abs/2306.16527
+However, due to an error on our side, we did not removed entire documents (i.e. URLs) which are opted out of AI model training. As of July 2023, we estimate that it represents 4 to 5% of the totality of OBELICS. The config `` (TODO) applies the correct filtering at the web document level. We recommend users of OBELICS to regularly check every document against the API.
 
-### GitHub repository
+## Content warnings
 
-https://github.com/huggingface/OBELISC
+TODO: give examples of some of the content that can raise eyebrows
 
 ## Terms of Use
 
@@ -112,10 +120,12 @@ License CC-BY-4.0.
 
 If you are using this dataset, please cite
 ```
-@inproceedings{
-lauren{\c{c}}on2023obe,
-title={OBELISC: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents},
-author={Hugo Lauren{\c{c}}on and Lucile Saulnier and L{\'e}o 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}
+@misc{laurençon2023obelisc,
+      title={OBELISC: 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}
 }
 ```