Bump version to 1.3.1.

PiperOrigin-RevId: 284192232

Author: adarob

docs/api_docs/python/tfds.md

@@ -78,4 +78,4 @@ from an image classification dataset.
 
 ## Other Members
 
-*   `__version__ = '1.3.0'` <a id="__version__"></a>
+*   `__version__ = '1.3.1'` <a id="__version__"></a>

docs/catalog/c4.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="c4" />
   <meta itemprop="description" content="A colossal, cleaned version of Common Crawl's web crawl corpus.&#10;&#10;Based on Common Crawl dataset: &quot;https://commoncrawl.org&quot;&#10;&#10;Due to the overhead of cleaning the dataset, it is recommend you prepare it with&#10;a distributed service like Cloud Dataflow. More info at&#10;https://www.tensorflow.org/datasets/beam_datasets.&#10;&#10;&#10;To use this dataset:&#10;&#10;```python&#10;import tensorflow_datasets as tfds&#10;&#10;ds = tfds.load('c4', 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="url" content="https://www.tensorflow.org/datasets/catalog/c4" />
   <meta itemprop="sameAs" content="https://github.com/google-research/text-to-text-transfer-transformer#datasets" />
   <meta itemprop="citation" content="&#10;@article{2019t5,&#10;  author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu},&#10;  title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer},&#10;  journal = {arXiv e-prints},&#10;  year = {2019},&#10;  archivePrefix = {arXiv},&#10;  eprint = {1910.10683},&#10;}&#10;" />
 </div>
-
 # `c4` (Manual download)
 
 A colossal, cleaned version of Common Crawl's web crawl corpus.

docs/catalog/oxford_flowers102.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="oxford_flowers102" />
   <meta itemprop="description" content="&#10;The Oxford Flowers 102 dataset is a consistent of 102 flower categories commonly occurring&#10;in the United Kingdom. Each class consists of between 40 and 258 images. The images have&#10;large scale, pose and light variations. In addition, there are categories that have large&#10;variations within the category and several very similar categories.&#10;&#10;The dataset is divided into a training set, a validation set and a test set.&#10;The training set and validation set each consist of 10 images per class (totalling 1020 images each).&#10;The test set consists of the remaining 6149 images (minimum 20 per class).&#10;&#10;&#10;To use this dataset:&#10;&#10;```python&#10;import tensorflow_datasets as tfds&#10;&#10;ds = tfds.load('oxford_flowers102', 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="url" content="https://www.tensorflow.org/datasets/catalog/oxford_flowers102" />
   <meta itemprop="sameAs" content="https://www.robots.ox.ac.uk/~vgg/data/flowers/102/" />
   <meta itemprop="citation" content="@InProceedings{Nilsback08,&#10;   author = &quot;Nilsback, M-E. and Zisserman, A.&quot;,&#10;   title = &quot;Automated Flower Classification over a Large Number of Classes&quot;,&#10;   booktitle = &quot;Proceedings of the Indian Conference on Computer Vision, Graphics and Image Processing&quot;,&#10;   year = &quot;2008&quot;,&#10;   month = &quot;Dec&quot;&#10;}&#10;" />
 </div>
-
 # `oxford_flowers102`
 
 The Oxford Flowers 102 dataset is a consistent of 102 flower categories commonly

docs/catalog/scicite.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="scicite" />
   <meta itemprop="description" content="&#10;This is a dataset for classifying citation intents in academic papers.&#10;The main citation intent label for each Json object is specified with the label&#10;key while the citation context is specified in with a context key. Example:&#10;{&#10; 'string': 'In chacma baboons, male-infant relationships can be linked to both&#10;    formation of friendships and paternity success [30,31].'&#10; 'sectionName': 'Introduction',&#10; 'label': 'background',&#10; 'citingPaperId': '7a6b2d4b405439',&#10; 'citedPaperId': '9d1abadc55b5e0',&#10; ...&#10; }&#10;You may obtain the full information about the paper using the provided paper ids&#10;with the Semantic Scholar API (https://api.semanticscholar.org/).&#10;The labels are:&#10;Method, Background, Result&#10;&#10;&#10;To use this dataset:&#10;&#10;```python&#10;import tensorflow_datasets as tfds&#10;&#10;ds = tfds.load('scicite', 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="url" content="https://www.tensorflow.org/datasets/catalog/scicite" />
   <meta itemprop="sameAs" content="https://github.com/allenai/scicite" />
   <meta itemprop="citation" content="&#10;@InProceedings{Cohan2019Structural,&#10;  author={Arman Cohan and Waleed Ammar and Madeleine Van Zuylen and Field Cady},&#10;  title={Structural Scaffolds for Citation Intent Classification in Scientific Publications},&#10;  booktitle=&quot;NAACL&quot;,&#10;  year=&quot;2019&quot;&#10;}&#10;" />
 </div>
-
 # `scicite`
 
 This is a dataset for classifying citation intents in academic papers. The main
@@ -34,7 +32,6 @@ using the provided paper ids with the Semantic Scholar API
 *   Size: `22.12 MiB`
 
 ## Features
-
 ```python
 FeaturesDict({
     'citeEnd': Tensor(shape=(), dtype=tf.int64),
@@ -68,11 +65,9 @@ VALIDATION | 916
 *   [https://github.com/allenai/scicite](https://github.com/allenai/scicite)
 
 ## Supervised keys (for `as_supervised=True`)
-
 `(u'string', u'label')`
 
 ## Citation
-
 ```
 @InProceedings{Cohan2019Structural,
   author={Arman Cohan and Waleed Ammar and Madeleine Van Zuylen and Field Cady},

tensorflow_datasets/version.py

@@ -18,7 +18,7 @@
 # We follow Semantic Versioning (https://semver.org/)
 _MAJOR_VERSION = '1'
 _MINOR_VERSION = '3'
-_PATCH_VERSION = '0'
+_PATCH_VERSION = '1'
 
 __version__ = '.'.join([
     _MAJOR_VERSION,