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Update the dataset creation docs #2629

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merged 3 commits into from
Nov 23, 2023
Merged

Update the dataset creation docs #2629

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de87d99
Update the dataset creation docs
adam-narozniak Nov 23, 2023
08b143a
Update datasets/doc/source/how-to-use-with-pytorch.rst
adam-narozniak Nov 23, 2023
b5c9887
Merge branch 'main' into fds-update-dataset-creation-docs
danieljanes Nov 23, 2023
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10 changes: 6 additions & 4 deletions datasets/doc/source/how-to-use-with-pytorch.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -10,7 +10,7 @@ Standard setup - download the dataset, choose the partitioning::
partition = fds.load_partition(0, "train")
centralized_dataset = fds.load_full("test")

Determine the names of our features (you can alternatively do that directly on the Hugging Face website). The name can
Determine the names of the features (you can alternatively do that directly on the Hugging Face website). The name can
vary e.g. "img" or "image", "label" or "labels"::

partition.features
Expand Down Expand Up @@ -38,7 +38,7 @@ That is why we iterate over all the samples from this batch and apply our transf
return batch

partition_torch = partition.with_transform(apply_transforms)
# At this point, you can check if you didn't make any mistakes by calling partition_torch[0]
# Now, you can check if you didn't make any mistakes by calling partition_torch[0]
dataloader = DataLoader(partition_torch, batch_size=64)


Expand Down Expand Up @@ -70,8 +70,10 @@ If you want to divide the dataset, you can use (at any point before passing the
Or you can simply calculate the indices yourself::

partition_len = len(partition)
partition_train = partition[:int(0.8 * partition_len)]
partition_test = partition[int(0.8 * partition_len):]
# Split `partition` 80:20
num_train_examples = int(0.8 * partition_len)
partition_train = partition.select(range(num_train_examples)) ) # use first 80%
partition_test = partition.select(range(num_train_examples, partition_len)) ) # use last 20%

And during the training loop, you need to apply one change. With a typical dataloader, you get a list returned for each iteration::

Expand Down