Automated documentation update

PiperOrigin-RevId: 303008577

Author: Conchylicultor

docs/catalog/beans.md

@@ -28,7 +28,7 @@ and collected by the Makerere AI research lab.
 *   **Dataset size**: `171.63 MiB`
 *   **Auto-cached**
     ([documentation](https://www.tensorflow.org/datasets/performances#auto-caching)):
-    Yes (test, validation), Only when `shuffle_files=False` (train)
+    Yes (validation, test), Only when `shuffle_files=False` (train)
 *   **Splits**:
 
 Split        | Examples

docs/catalog/big_patent.md

@@ -125,7 +125,7 @@ Split        | Examples
 *   **Dataset size**: `170.08 MiB`
 *   **Auto-cached**
     ([documentation](https://www.tensorflow.org/datasets/performances#auto-caching)):
-    Yes (test, validation), Only when `shuffle_files=False` (train)
+    Yes (validation, test), Only when `shuffle_files=False` (train)
 *   **Splits**:
 
 Split        | Examples

docs/catalog/cifar10.md

@@ -20,9 +20,9 @@ The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with
 *   **Source code**:
     [`tfds.image.cifar.Cifar10`](https://github.com/tensorflow/datasets/tree/master/tensorflow_datasets/image/cifar.py)
 *   **Versions**:
-    *   **`3.0.1`** (default): No release notes.
+    *   **`3.0.2`** (default): No release notes.
 *   **Download size**: `162.17 MiB`
-*   **Dataset size**: `131.10 MiB`
+*   **Dataset size**: `132.40 MiB`
 *   **Auto-cached**
     ([documentation](https://www.tensorflow.org/datasets/performances#auto-caching)):
     Yes
@@ -37,10 +37,12 @@ Split   | Examples
 
 ```python
 FeaturesDict({
+    'id': Text(shape=(), dtype=tf.string),
     'image': Image(shape=(32, 32, 3), dtype=tf.uint8),
     'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=10),
 })
 ```
+
 *   **Supervised keys** (See
     [`as_supervised` doc](https://www.tensorflow.org/datasets/api_docs/python/tfds/load#args)):
     `('image', 'label')`

docs/catalog/cifar100.md

@@ -23,9 +23,9 @@ superclass to which it belongs).
 *   **Source code**:
     [`tfds.image.cifar.Cifar100`](https://github.com/tensorflow/datasets/tree/master/tensorflow_datasets/image/cifar.py)
 *   **Versions**:
-    *   **`3.0.1`** (default): No release notes.
+    *   **`3.0.2`** (default): No release notes.
 *   **Download size**: `160.71 MiB`
-*   **Dataset size**: `130.73 MiB`
+*   **Dataset size**: `132.03 MiB`
 *   **Auto-cached**
     ([documentation](https://www.tensorflow.org/datasets/performances#auto-caching)):
     Yes
@@ -41,10 +41,12 @@ Split   | Examples
 ```python
 FeaturesDict({
     'coarse_label': ClassLabel(shape=(), dtype=tf.int64, num_classes=20),
+    'id': Text(shape=(), dtype=tf.string),
     'image': Image(shape=(32, 32, 3), dtype=tf.uint8),
     'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=100),
 })
 ```
+
 *   **Supervised keys** (See
     [`as_supervised` doc](https://www.tensorflow.org/datasets/api_docs/python/tfds/load#args)):
     `('image', 'label')`

docs/catalog/imagenet2012_subset.md

@@ -2,14 +2,12 @@
   <div itemscope itemprop="includedInDataCatalog" itemtype="http://schema.org/DataCatalog">
     <meta itemprop="name" content="TensorFlow Datasets" />
   </div>
-
   <meta itemprop="name" content="imagenet2012_subset" />
   <meta itemprop="description" content="Imagenet2012Subset is a subset of original ImageNet ILSVRC 2012 dataset.&#10;The dataset share the *same* validation set as the original ImageNet ILSVRC 2012&#10;dataset. However, the training set is subsampled in a label balanced fashion.&#10;In `1pct` configuration, 1%, or 12811, images are sampled, most classes have&#10;the same number of images (average 12.8), some classes randomly have 1 more&#10;example than others; and in `10pct` configuration, ~10%, or 128116, most classes&#10;have the same number of images (average 128), and some classes randomly have 1&#10;more example than others.&#10;&#10;This is supposed to be used as a benchmark for semi-supervised learning, and&#10;has been originally used in SimCLR paper (https://arxiv.org/abs/2002.05709).&#10;&#10;&#10;To use this dataset:&#10;&#10;```python&#10;import tensorflow_datasets as tfds&#10;&#10;ds = tfds.load(&#x27;imagenet2012_subset&#x27;, split=&#x27;train&#x27;)&#10;for ex in ds.take(4):&#10;  print(ex)&#10;```&#10;&#10;See [the guide](https://www.tensorflow.org/datasets/overview) for more&#10;informations on [tensorflow_datasets](https://www.tensorflow.org/datasets).&#10;&#10;" />
   <meta itemprop="url" content="https://www.tensorflow.org/datasets/catalog/imagenet2012_subset" />
   <meta itemprop="sameAs" content="http://image-net.org/" />
   <meta itemprop="citation" content="@article{chen2020simple,&#10;  title={A Simple Framework for Contrastive Learning of Visual Representations},&#10;  author={Chen, Ting and Kornblith, Simon and Norouzi, Mohammad and Hinton, Geoffrey},&#10;  journal={arXiv preprint arXiv:2002.05709},&#10;  year={2020}&#10;}&#10;@article{ILSVRC15,&#10;  Author = {Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei},&#10;  Title = {{ImageNet Large Scale Visual Recognition Challenge}},&#10;  Year = {2015},&#10;  journal   = {International Journal of Computer Vision (IJCV)},&#10;  doi = {10.1007/s11263-015-0816-y},&#10;  volume={115},&#10;  number={3},&#10;  pages={211-252}&#10;}&#10;" />
 </div>
-
 # `imagenet2012_subset`
 
 Warning: Manual download required. See instructions below.
@@ -59,7 +57,6 @@ FeaturesDict({
     'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=1000),
 })
 ```
-
 *   **Supervised keys** (See
     [`as_supervised` doc](https://www.tensorflow.org/datasets/api_docs/python/tfds/load#args)):
     `('image', 'label')`