Update README.md

README.md

@@ -52,27 +52,29 @@ size_categories:
 |v0.1| Release of the Fondant-cc-25m dataset
 
 ## Dataset Summary
-Fondant-cc-25m contains 25 million image URLs available for use under [Creative Commons](https://creativecommons.org/) license collected from the [Common Crawl web corpus](https://commoncrawl.org/). The dataset was created as part of [Fondant](https://fondant.ai), a open-source framework for building composable data preparation pipelines for training (and fine-tuning) foundation models.
+Fondant-cc-25m contains 25 million image URLs available under different [Creative Commons](https://creativecommons.org/) 
+licenses collected from the [Common Crawl web corpus](https://commoncrawl.org/). 
+The dataset was created using [Fondant](https://fondant.ai), an open-source framework for building composable data 
+preparation pipelines for training (and fine-tuning) foundation models.
 
 ## Supported Tasks and Leaderboards
 
-[TODO]?
+This dataset can be used for training or fine-tuning image generation or computer vision models.
 
 ## How to use it
 
-We have prepared a sample fondant pipeline that limits the dataset to PNG files and subsequently
-downloads the images.
+We have prepared a sample fondant pipeline that limits the dataset to PNG files and downloads the images.
 
-To execute the pipeline locally, you must have [docker compose](https://docs.docker.com/compose/) installed on your local
+To execute the pipeline locally, you must have [docker compose](https://docs.docker.com/compose/) installed on your 
 system.
 
-Furthermore, you will need to install Fondant by running:
+You can then install Fondant by running:
 
 ```
 pip install fondant[pipelines]
 ```
 
-Then you can clone the GitHub repository, navigate to the `examples/pipelines/filter-cc-25m`, and initiate the
+Then you can clone the GitHub repository, navigate to `examples/pipelines/filter-cc-25m`, and initiate the
 pipeline by executing:
 
 ```bash
@@ -87,18 +89,20 @@ fondant explore --base_path ./data
 
 ## Dataset Structure
 ### Data Instances
-Each data instance corresponds to one image. The URL of the image is in the `image_url` feature, and other features (`alt_text`, `webpage_url`, etc) provide some metadata. Note that images have been deduplicated only based on their URLs and not embeddings.
+Each data instance corresponds to one image. The URL of the image is in the `image_url` feature, and other features (`alt_text`, `webpage_url`, etc) provide some 
+metadata. Note that images have been deduplicated only based on their URLs.
 
 ### Data Fields
 - `image_url` (string): image url to download the image
 - `alt_text` (string): alternative text of the image
 - `webpage_url` (string): webpage source of the image
-- `license_type` (string): creative commons licence type of the image
+- `license_type` (string): creative commons license type of the image
 - `license_location` (string): location of the license on the webpage
-- `surt_url` (string): sort friendly image url with top level domain as the prefix
+- `sort_url` (string): sort friendly image url with top level domain as the prefix
 
 ## Data Splits
-The dataset has no splits and all data is loaded as a [TODO: check split name - train?] split by default. If you want to setup a custom train-test split beware that the dataset may contain duplicates which can cause leakage into the test split.
+The dataset has no splits and all data is loaded as a [TODO: check split name - train?] split by default. If you want to setup a custom train-test split 
+beware that the dataset may contain duplicates which can cause leakage into the test split.
 
 
 ## Dataset Creation
@@ -111,7 +115,8 @@ This dataset is the first step towards our final goal of a 500M Creative Commons
 ### Source Data
 #### Initial Data Collection and Normalization
 ##### License detection 
-Permissive licenses have minimal restrictions on how the image can be copied, modified, and redistributed. The full list of licenses can be found [here](https://creativecommons.org/about/cclicenses/).
+Permissive licenses have minimal restrictions on how the image can be copied, modified, and redistributed. 
+The full list of licenses can be found [here](https://creativecommons.org/about/cclicenses/).
 
 We examined HTML tags of the webpages for the presence of Creative Commons license URLs. A webpage was marked permissive if only a license URL was found in its footer, aside or sidebar. Subsequently, all the image URLs present on the web page were collected together with the license information.
 This approach achieved a 96.32% accuracy in an earlier test where we investigated the scope of licenses. More information on our approach can be found in [this blogpost](https://blog.ml6.eu/ai-image-generation-without-copyright-infringement-a9901b64541c).