Add dataset creation details to dataset card

README.md

@@ -69,15 +69,58 @@ We provide an [example use case](https://github.com/ml6team/fondant-usecase-cont
 
 <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
 
-The data repository is 
-structured as follows:
-- [data/](https://huggingface.co/datasets/fondant-ai/datacomp-small-clip/viewer): The dataset 
+The data repository is structured as follows:
+- [data/](https://huggingface.co/datasets/fondant-ai/datacomp-small-clip/viewer/embeddings): The dataset 
   containing ids, urls, and CLIP embeddings
 - [faiss](https://huggingface.co/datasets/fondant-ai/datacomp-small-clip/blob/main/faiss): 
   The faiss index
-- [id_mapping/](https://huggingface.co/datasets/fondant-ai/datacomp-small-clip/tree/main/id_mapping): 
+- [id_mapping/](https://huggingface.co/datasets/fondant-ai/datacomp-small-clip/viewer/id_mapping): 
   The mapping of the faiss ids to the original urls
 
+## Dataset Creation
+
+We leveraged Fondant to generate the CLIP index and published the pipeline as a 
+[git repository](https://github.com/ml6team/fondant-clip-index). The pipeline consists of 4 steps:
+
+- A [`load_from_hf_hub`](https://fondant.ai/en/stable/components/hub/#load_from_hf_hub#description)
+  operation that loads the
+  [datacomp_small](https://huggingface.co/datasets/mlfoundations/datacomp_small) dataset from
+  huggingface into the Fondant workspace and format.
+- A [`download_images`](https://fondant.ai/en/stable/components/hub/#download_images#description)
+  operation which downloads the actual images from the urls in the dataset.
+- A [`embed_images`](https://fondant.ai/en/stable/components/hub/#embed_images#description) operation which embeds the downloaded images using a CLIP model.
+- A [`write_to_file`](https://fondant.ai/en/stable/components/hub/#write_to_file#description)
+  operation which writes the original urls and generated embeddings to the chosen destination.
+
+After running the pipeline, we used [`autofaiss`](https://github.com/criteo/autofaiss) to build the
+CLIP index.
+
+### Execution details
+
+### Download images
+
+We downloaded the images with 32 cores in parallel, each opening up to 25 concurrent connections,
+and achieved a success rate of 72%, resulting in 9.251.172 images.
+
+The downloading was executed on a VM on GCP using the Fondant Docker runner. We originally
+planned to run this on Vertex AI, but moved to a VM when noticing lower network bandwidth on Vertex.
+
+The success rate can probably be further improved by setting up a faster DNS resolver.
+
+### Embed images
+
+We leveraged the
+[`laion/CLIP-ViT-B-32-laion2B-s34B-b79K`](https://huggingface.co/laion/CLIP-ViT-B-32-laion2B-s34B-b79K)
+CLIP model. We chose this model because of a couple of reasons. It is popular, which makes it
+easy to use with existing embeddings, it is small, which makes it cheap to run, and it is an open
+model trained on open data.
+
+We appreciate any feedback on our choice of model, so we can take this into account if we
+generate indices for larger datasets in the future.
+
+The embedding was executed on 4 T4 GPUs on Google Cloud using our Vertex AI runner, with a batch
+size of 32. The execution took 8:15 hours.
+
 ## Terms and Conditions
 Under no circumstances can Fondant be held liable by a third party for (i) the accuracy or correctness of the content, (ii) an alleged infringement of intellectual property rights or (iii) any other alleged claim, action, injunction or suit resulting from the publication or use of the dataset.