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  ## Supported Tasks and Leaderboards
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  ## How to use it
 
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  ```python
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  from datasets import load_dataset
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@@ -58,7 +59,33 @@ ds = load_dataset("fondantai/cc-25m", streaming=True)
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  print(next(iter(dataset))) #TODO: add int ids?
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  ```
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- TODO: How to use in a fondant pipeline?
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ## Dataset Structure
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  ### Data Instances
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  ## Additional Information
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  ### Dataset Curators
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- 1. Robbe Sneyders, ML6, robbe.sneyders@ml6.eu
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- 2. Sharon Grundmann, ML6, sharon.grundmann@ml6.eu
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  3. Matthias Richter, ML6, matthias.richter@ml6.eu
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  ## Supported Tasks and Leaderboards
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  ## How to use it
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+
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  ```python
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  from datasets import load_dataset
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  print(next(iter(dataset))) #TODO: add int ids?
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  ```
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+ ### Utilizing the dataset in a fondant pipeline
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+
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+ We have prepared a sample fondant pipeline that reduces the dataset to PNG files and subsequently
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+ downloads the images.
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+ To execute the pipeline locally, you must have [docker compose](https://docs.docker.com/compose/) installed on your local
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+ system.
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+ Furthermore, you will need to install Fondant by running:
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+
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+ ```
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+ pip install fondant[pipelines]
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+ ```
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+
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+ Afterward, you can clone the GitHub repository, navigate to the `examples/pipelines/filter-cc-25m`, and initiate the
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+ pipeline by executing:
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+ ```bash
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+ fondant run pipeline --local
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+ ```
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+ To visually inspect the results quickly, you can use:
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+
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+ ```bash
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+ fondant explore --base_path ./data
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+ ```
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  ## Dataset Structure
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  ### Data Instances
 
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  ## Additional Information
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  ### Dataset Curators
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+ 1. Sharon Grundmann, ML6, sharon.grundmann@ml6.eu
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+ 2. Robbe Sneyders, ML6, robbe.sneyders@ml6.eu
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  3. Matthias Richter, ML6, matthias.richter@ml6.eu
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