Automated documentation update

PiperOrigin-RevId: 291039835

Author: Conchylicultor

docs/api_docs/python/tfds/as_numpy.md

@@ -34,6 +34,16 @@ are left as-is for the user to deal with them (e.g. using `to_list()`). In TF 1
 (i.e. graph mode), `tf.RaggedTensor`s are returned as
 `tf.ragged.RaggedTensorValue`s.
 
+#### Example:
+
+```
+ds = tfds.load(name="mnist", split="train")
+ds_numpy = tfds.as_numpy(ds)  # Convert `tf.data.Dataset` to Python generator
+for ex in ds_numpy:
+  # `{'image': np.array(shape=(28, 28, 1)), 'labels': np.array(shape=())}`
+  print(ex)
+```
+
 #### Args:
 
 *   <b>`dataset`</b>: a possibly nested structure of `tf.data.Dataset`s and/or

docs/api_docs/python/tfds/core/SplitGenerator.md

@@ -30,9 +30,10 @@ of usage.
 <a target="_blank" href="https://github.com/tensorflow/datasets/tree/master/tensorflow_datasets/core/splits.py">View
 source</a>
 
-``` python
+```python
 __init__(
     name,
+    num_shards=1,
     gen_kwargs=None
 )
 ```

docs/catalog/_toc.yaml

@@ -82,6 +82,8 @@ toc:
     title: eurosat
   - path: /datasets/catalog/fashion_mnist
     title: fashion_mnist
+  - path: /datasets/catalog/flic
+    title: flic
   - path: /datasets/catalog/food101
     title: food101
   - path: /datasets/catalog/horses_or_humans

docs/catalog/abstract_reasoning.md

@@ -2,12 +2,14 @@
   <div itemscope itemprop="includedInDataCatalog" itemtype="http://schema.org/DataCatalog">
     <meta itemprop="name" content="TensorFlow Datasets" />
   </div>
+
   <meta itemprop="name" content="aeslc" />
-  <meta itemprop="description" content="&#10;A collection of email messages of employees in the Enron Corporation.&#10;&#10;There are two features:&#10;  - email_body: email body text.&#10;  - subject_line: email subject text.&#10;&#10;&#10;To use this dataset:&#10;&#10;```python&#10;import tensorflow_datasets as tfds&#10;&#10;ds = tfds.load('aeslc', split='train')&#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="description" content="&#10;A collection of email messages of employees in the Enron Corporation.&#10;&#10;There are two features:&#10;  - email_body: email body text.&#10;  - subject_line: email subject text.&#10;&#10;&#10;To use this dataset:&#10;&#10;```python&#10;import tensorflow_datasets as tfds&#10;&#10;ds = tfds.load(&#x27;aeslc&#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/aeslc" />
   <meta itemprop="sameAs" content="https://github.com/ryanzhumich/AESLC" />
   <meta itemprop="citation" content="&#10;@misc{zhang2019email,&#10;    title={This Email Could Save Your Life: Introducing the Task of Email Subject Line Generation},&#10;    author={Rui Zhang and Joel Tetreault},&#10;    year={2019},&#10;    eprint={1906.03497},&#10;    archivePrefix={arXiv},&#10;    primaryClass={cs.CL}&#10;}&#10;" />
 </div>
+
 # `aeslc`
 
 A collection of email messages of employees in the Enron Corporation.

docs/catalog/aeslc.md

@@ -2,12 +2,14 @@
   <div itemscope itemprop="includedInDataCatalog" itemtype="http://schema.org/DataCatalog">
     <meta itemprop="name" content="TensorFlow Datasets" />
   </div>
+
   <meta itemprop="name" content="aflw2k3d" />
-  <meta itemprop="description" content="AFLW2000-3D is a dataset of 2000 images that have been annotated with image-level&#10;68-point 3D facial landmarks.&#10;This dataset is typically used for evaluation of 3D facial landmark detection&#10;models. The head poses are very diverse and often hard to be detected by a &#10;cnn-based face detector.&#10;The 2D landmarks are skipped in this dataset, since some of the data are not&#10;consistent to 21 points, as the original paper mentioned.&#10;&#10;&#10;To use this dataset:&#10;&#10;```python&#10;import tensorflow_datasets as tfds&#10;&#10;ds = tfds.load('aflw2k3d', split='train')&#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="description" content="AFLW2000-3D is a dataset of 2000 images that have been annotated with image-level&#10;68-point 3D facial landmarks.&#10;This dataset is typically used for evaluation of 3D facial landmark detection&#10;models. The head poses are very diverse and often hard to be detected by a &#10;cnn-based face detector.&#10;The 2D landmarks are skipped in this dataset, since some of the data are not&#10;consistent to 21 points, as the original paper mentioned.&#10;&#10;&#10;To use this dataset:&#10;&#10;```python&#10;import tensorflow_datasets as tfds&#10;&#10;ds = tfds.load(&#x27;aflw2k3d&#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/aflw2k3d" />
   <meta itemprop="sameAs" content="http://www.cbsr.ia.ac.cn/users/xiangyuzhu/projects/3DDFA/main.htm" />
   <meta itemprop="citation" content="@article{DBLP:journals/corr/ZhuLLSL15,&#10;  author    = {Xiangyu Zhu and&#10;               Zhen Lei and&#10;               Xiaoming Liu and&#10;               Hailin Shi and&#10;               Stan Z. Li},&#10;  title     = {Face Alignment Across Large Poses: {A} 3D Solution},&#10;  journal   = {CoRR},&#10;  volume    = {abs/1511.07212},&#10;  year      = {2015},&#10;  url       = {http://arxiv.org/abs/1511.07212},&#10;  archivePrefix = {arXiv},&#10;  eprint    = {1511.07212},&#10;  timestamp = {Mon, 13 Aug 2018 16:48:23 +0200},&#10;  biburl    = {https://dblp.org/rec/bib/journals/corr/ZhuLLSL15},&#10;  bibsource = {dblp computer science bibliography, https://dblp.org}&#10;}&#10;" />
 </div>
+
 # `aflw2k3d`
 
 AFLW2000-3D is a dataset of 2000 images that have been annotated with

docs/catalog/aflw2k3d.md

@@ -2,12 +2,14 @@
   <div itemscope itemprop="includedInDataCatalog" itemtype="http://schema.org/DataCatalog">
     <meta itemprop="name" content="TensorFlow Datasets" />
   </div>
+
   <meta itemprop="name" content="amazon_us_reviews" />
-  <meta itemprop="description" content="Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.&#10;&#10;Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).&#10;&#10;Each Dataset contains the following columns : &#10;  marketplace       - 2 letter country code of the marketplace where the review was written.&#10;  customer_id       - Random identifier that can be used to aggregate reviews written by a single author.&#10;  review_id         - The unique ID of the review.&#10;  product_id        - The unique Product ID the review pertains to. In the multilingual dataset the reviews&#10;                      for the same product in different countries can be grouped by the same product_id.&#10;  product_parent    - Random identifier that can be used to aggregate reviews for the same product.&#10;  product_title     - Title of the product.&#10;  product_category  - Broad product category that can be used to group reviews &#10;                      (also used to group the dataset into coherent parts).&#10;  star_rating       - The 1-5 star rating of the review.&#10;  helpful_votes     - Number of helpful votes.&#10;  total_votes       - Number of total votes the review received.&#10;  vine              - Review was written as part of the Vine program.&#10;  verified_purchase - The review is on a verified purchase.&#10;  review_headline   - The title of the review.&#10;  review_body       - The review text.&#10;  review_date       - The date the review was written.&#10;&#10;&#10;To use this dataset:&#10;&#10;```python&#10;import tensorflow_datasets as tfds&#10;&#10;ds = tfds.load('amazon_us_reviews', split='train')&#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="description" content="Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.&#10;&#10;Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).&#10;&#10;Each Dataset contains the following columns : &#10;  marketplace       - 2 letter country code of the marketplace where the review was written.&#10;  customer_id       - Random identifier that can be used to aggregate reviews written by a single author.&#10;  review_id         - The unique ID of the review.&#10;  product_id        - The unique Product ID the review pertains to. In the multilingual dataset the reviews&#10;                      for the same product in different countries can be grouped by the same product_id.&#10;  product_parent    - Random identifier that can be used to aggregate reviews for the same product.&#10;  product_title     - Title of the product.&#10;  product_category  - Broad product category that can be used to group reviews &#10;                      (also used to group the dataset into coherent parts).&#10;  star_rating       - The 1-5 star rating of the review.&#10;  helpful_votes     - Number of helpful votes.&#10;  total_votes       - Number of total votes the review received.&#10;  vine              - Review was written as part of the Vine program.&#10;  verified_purchase - The review is on a verified purchase.&#10;  review_headline   - The title of the review.&#10;  review_body       - The review text.&#10;  review_date       - The date the review was written.&#10;&#10;&#10;To use this dataset:&#10;&#10;```python&#10;import tensorflow_datasets as tfds&#10;&#10;ds = tfds.load(&#x27;amazon_us_reviews&#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/amazon_us_reviews" />
   <meta itemprop="sameAs" content="https://s3.amazonaws.com/amazon-reviews-pds/readme.html" />
   <meta itemprop="citation" content="" />
 </div>
+
 # `amazon_us_reviews`
 
 Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic

docs/catalog/amazon_us_reviews.md

@@ -2,12 +2,14 @@
   <div itemscope itemprop="includedInDataCatalog" itemtype="http://schema.org/DataCatalog">
     <meta itemprop="name" content="TensorFlow Datasets" />
   </div>
+
   <meta itemprop="name" content="arc" />
-  <meta itemprop="description" content="&#10;ARC can be seen as a general artificial intelligence benchmark, as a program&#10;synthesis benchmark, or as a psychometric intelligence test. It is targeted at&#10;both humans and artificially intelligent systems that aim at emulating a&#10;human-like form of general fluid intelligence.&#10;&#10;&#10;To use this dataset:&#10;&#10;```python&#10;import tensorflow_datasets as tfds&#10;&#10;ds = tfds.load('arc', split='train')&#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="description" content="&#10;ARC can be seen as a general artificial intelligence benchmark, as a program&#10;synthesis benchmark, or as a psychometric intelligence test. It is targeted at&#10;both humans and artificially intelligent systems that aim at emulating a&#10;human-like form of general fluid intelligence.&#10;&#10;&#10;To use this dataset:&#10;&#10;```python&#10;import tensorflow_datasets as tfds&#10;&#10;ds = tfds.load(&#x27;arc&#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/arc" />
   <meta itemprop="sameAs" content="https://github.com/fchollet/ARC/" />
   <meta itemprop="citation" content="&#10;@misc{chollet_francois_2019,&#10;  title     = {The Measure of Intelligence},&#10;  url       = {https://arxiv.org/abs/1911.01547},&#10;  journal   = {arXiv.org},&#10;  author    = {Francois Chollet},&#10;  year      = {2019},&#10;  month     = {Nov}&#10;}&#10;" />
 </div>
+
 # `arc`
 
 ARC can be seen as a general artificial intelligence benchmark, as a program

docs/catalog/arc.md

@@ -2,12 +2,14 @@
   <div itemscope itemprop="includedInDataCatalog" itemtype="http://schema.org/DataCatalog">
     <meta itemprop="name" content="TensorFlow Datasets" />
   </div>
+
   <meta itemprop="name" content="bair_robot_pushing_small" />
-  <meta itemprop="description" content="This data set contains roughly 44,000 examples of robot pushing motions, including one training set (train) and two test sets of previously seen (testseen) and unseen (testnovel) objects. This is the small 64x64 version.&#10;&#10;To use this dataset:&#10;&#10;```python&#10;import tensorflow_datasets as tfds&#10;&#10;ds = tfds.load('bair_robot_pushing_small', split='train')&#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="description" content="This data set contains roughly 44,000 examples of robot pushing motions, including one training set (train) and two test sets of previously seen (testseen) and unseen (testnovel) objects. This is the small 64x64 version.&#10;&#10;To use this dataset:&#10;&#10;```python&#10;import tensorflow_datasets as tfds&#10;&#10;ds = tfds.load(&#x27;bair_robot_pushing_small&#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/bair_robot_pushing_small" />
   <meta itemprop="sameAs" content="https://sites.google.com/view/sna-visual-mpc/" />
   <meta itemprop="citation" content="@misc{1710.05268,&#10;  Author = {Frederik Ebert and Chelsea Finn and Alex X. Lee and Sergey Levine},&#10;  Title = {Self-Supervised Visual Planning with Temporal Skip Connections},&#10;  Year = {2017},&#10;  Eprint = {arXiv:1710.05268},&#10;}&#10;" />
 </div>
+
 # `bair_robot_pushing_small`
 
 This data set contains roughly 44,000 examples of robot pushing motions,
@@ -18,11 +20,12 @@ including one training set (train) and two test sets of previously seen
     [https://sites.google.com/view/sna-visual-mpc/](https://sites.google.com/view/sna-visual-mpc/)
 *   `DatasetBuilder`:
     [`tfds.video.bair_robot_pushing.BairRobotPushingSmall`](https://github.com/tensorflow/datasets/tree/master/tensorflow_datasets/video/bair_robot_pushing.py)
-*   Version: `v1.0.0`
+*   Version: `v2.0.0`
 *   Versions:
 
-    *   **`1.0.0`** (default):
-    *   `2.0.0`: New split API (https://tensorflow.org/datasets/splits)
+    *   **`2.0.0`** (default): New split API
+        (https://tensorflow.org/datasets/splits)
+    *   `1.0.0`: None
 
 *   Size: `30.06 GiB`