Various changes

README.md

@@ -119,17 +119,18 @@ python extract_images.py --parquet_file <Path to the Parquet file or folder cont
 If you want to contribute to the dataset, the best way is to help us develop pipeline components for further processing. Components
 we are currently looking to add are the following ([GitHub issues](https://github.com/ml6team/fondant/issues?q=is%3Aissue+is%3Aopen+label%3A%22Component+Contribution%22)):
 - Image-based deduplication
-- Visual quality / aesthetic quality estimation
 - Automatic captioning
-- Not safe for work (NSFW) content detection
+- Visual quality / aesthetic quality estimation
 - Watermark detection
+- Not safe for work (NSFW) content detection
+- CLIP embedding generation
 - Face detection
 - Personal Identifiable Information (PII) detection
 - Text detection
 - AI generated image detection
-- CLIP embedding generation
 - Image-text CLIP similarity
-- ...
+- Any components that you propose to develop\
+
 We are also looking for core framework contributors and users who are willing to give feedback on usability and suggest potential improvements
 
 
@@ -165,11 +166,11 @@ fondant-cc-25m is built from CommonCrawl dumps. These dumps are constructed from
 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 only when a license URL was found in 
-its footer, aside or sidebar. This was the case only in around 0.164% of a 100k sample set which suggests that image generation models trained on a random sample from
-the public internet may be trained on up to 99.836% images that may be copyrighted.
+its footer, aside or sidebar. This was the case only in around 0.164% of a 100k random sample from Common Crawl. This suggests that image generation models 
+trained on a random sample from the public internet may be trained on up to 99.836% copyrighted images.
 
-Subsequently, all the image URLs present on the web page were collected together with the license information.
-A manual test of 1032 randomly sampled images showed an accuracy of 96.32% in which case the image was actually released under a Creative Commons license.
+Subsequently, all the image URLs present on the web page were collected together with the license information. A manual check of a random
+sample of 1032 images showed that 96.32% were attributed the correct license whil 3.68% were not.
 False positives could be due to parsing errors but also incorrect attributions: images indicated by the publisher to be CC which are not.
 More information on our approach can be found in [this blogpost](https://blog.ml6.eu/ai-image-generation-without-copyright-infringement-a9901b64541c).