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README.md

@@ -74,12 +74,31 @@ This repo also provide scripts to explore the images and labels in more detail.
 
 ## Prepare the dataset in HDF5
 We found a single HDF5 file to be efficient for FL.
-If you want to process the dataset for general usage in FL, we recommend using [this preprocessing script](https://github.com/apple/pfl-research/blob/develop/benchmarks/dataset/flair/prepare_dataset.py) to construct a HDF5 file.
+If you want to process the dataset for general usage in FL, we recommend using [this preprocessing script](https://github.com/apple/pfl-research/blob/develop/benchmarks/dataset/flair/download_preprocess.py) to construct a HDF5 file.
 
 By default the script will group the images and labels by train/val/test split and then by user ids, making it suitable for federated learning experiments.
 With the flag `--not_group_data_by_user`, the script will simply group the images and labels by train/val/test split and ignore the user ids, which is the typical setup for centralized training. \
 ⚠️ Warning: the hdf5 file take up to ~80GB disk space to store after processing.
 
+## Use dataset directly with HuggingFace
+
+The dataset can also be used with the `datasets` package.
+To group datapoints by user, simply construct a mapping and then query the dataset by index:
+```
+from datasets import load_dataset
+from collections import defaultdict
+ds = load_dataset('apple/flair', split='val')
+
+user_to_ix = defaultdict(list)
+for i, record in enumerate(ds):
+    user_to_ix[record['user_id']].append(i)
+
+def load_user_data(user_id):
+    return [ds[i] for i in user_to_ix[user_id]]
+
+load_user_data('81594342@N00')
+```
+
 ## Disclaimer
 The annotations and Apple’s other rights in the dataset are licensed under CC-BY-NC 4.0 license. 
 The images are copyright of the respective owners, the license terms of which can be found using the links provided in ATTRIBUTIONS.TXT (by matching the Image ID).