Automated documentation update

PiperOrigin-RevId: 281137731

Author: Conchylicultor

docs/catalog/_toc.yaml

@@ -206,6 +206,8 @@ toc:
     title: c4
   - path: /datasets/catalog/definite_pronoun_resolution
     title: definite_pronoun_resolution
+  - path: /datasets/catalog/esnli
+    title: esnli
   - path: /datasets/catalog/gap
     title: gap
   - path: /datasets/catalog/glue

docs/catalog/dmlab.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="dmlab" />
   <meta itemprop="description" content="&#10;        The Dmlab dataset contains frames observed by the agent acting in the&#10;        DeepMind Lab environment, which are annotated by the distance between&#10;        the agent and various objects present in the environment. The goal is to&#10;        is to evaluate the ability of a visual model to reason about distances&#10;        from the visual input in 3D environments. The Dmlab dataset consists of&#10;        360x480 color images in 6 classes. The classes are&#10;        {close, far, very far} x {positive reward, negative reward}&#10;        respectively.&#10;&#10;To use this dataset:&#10;&#10;```python&#10;import tensorflow_datasets as tfds&#10;&#10;ds = tfds.load('dmlab', 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/dmlab" />
   <meta itemprop="sameAs" content="https://github.com/google-research/task_adaptation" />
   <meta itemprop="citation" content="@article{zhai2019visual,&#10;        title={The Visual Task Adaptation Benchmark},&#10;        author={Xiaohua Zhai and Joan Puigcerver and Alexander Kolesnikov and&#10;               Pierre Ruyssen and Carlos Riquelme and Mario Lucic and&#10;               Josip Djolonga and Andre Susano Pinto and Maxim Neumann and&#10;               Alexey Dosovitskiy and Lucas Beyer and Olivier Bachem and&#10;               Michael Tschannen and Marcin Michalski and Olivier Bousquet and&#10;               Sylvain Gelly and Neil Houlsby},&#10;                              year={2019},&#10;                              eprint={1910.04867},&#10;                              archivePrefix={arXiv},&#10;                              primaryClass={cs.CV},&#10;                              url = {https://arxiv.org/abs/1910.04867}&#10;                          }" />
 </div>
-
 # `dmlab`
 
 The Dmlab dataset contains frames observed by the agent acting in the DeepMind
@@ -32,7 +30,6 @@ respectively.
 *   Size: `2.81 GiB`
 
 ## Features
-
 ```python
 FeaturesDict({
     'filename': Text(shape=(), dtype=tf.string),
@@ -55,11 +52,9 @@ VALIDATION | 22,628
 *   [https://github.com/google-research/task_adaptation](https://github.com/google-research/task_adaptation)
 
 ## Supervised keys (for `as_supervised=True`)
-
 `(u'image', u'label')`
 
 ## Citation
-
 ```
 @article{zhai2019visual,
         title={The Visual Task Adaptation Benchmark},

docs/catalog/duke_ultrasound.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="duke_ultrasound" />
   <meta itemprop="description" content="DukeUltrasound is an ultrasound dataset collected at Duke University with a &#10;Verasonics c52v probe. It contains delay-and-sum (DAS) beamformed data &#10;as well as data post-processed with Siemens Dynamic TCE for speckle &#10;reduction, contrast enhancement and improvement in conspicuity of &#10;anatomical structures. These data were collected with support from the&#10;National Institute of Biomedical Imaging and Bioengineering under Grant &#10;R01-EB026574 and National Institutes of Health under Grant 5T32GM007171-44.&#10;A usage example is avalible &#10;[here](https://colab.research.google.com/drive/1R_ARqpWoiHcUQWg1Fxwyx-ZkLi0IZ5qs).&#10;&#10;To use this dataset:&#10;&#10;```python&#10;import tensorflow_datasets as tfds&#10;&#10;ds = tfds.load('duke_ultrasound', 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/duke_ultrasound" />
   <meta itemprop="sameAs" content="https://github.com/ouwen/mimicknet" />
   <meta itemprop="citation" content="@article{DBLP:journals/corr/abs-1908-05782,&#10;  author    = {Ouwen Huang and&#10;               Will Long and&#10;               Nick Bottenus and&#10;               Gregg E. Trahey and&#10;               Sina Farsiu and&#10;               Mark L. Palmeri},&#10;  title     = {MimickNet, Matching Clinical Post-Processing Under Realistic Black-Box&#10;               Constraints},&#10;  journal   = {CoRR},&#10;  volume    = {abs/1908.05782},&#10;  year      = {2019},&#10;  url       = {http://arxiv.org/abs/1908.05782},&#10;  archivePrefix = {arXiv},&#10;  eprint    = {1908.05782},&#10;  timestamp = {Mon, 19 Aug 2019 13:21:03 +0200},&#10;  biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1908-05782},&#10;  bibsource = {dblp computer science bibliography, https://dblp.org}&#10;}" />
 </div>
-
 # `duke_ultrasound`
 
 DukeUltrasound is an ultrasound dataset collected at Duke University with a

docs/catalog/esnli.md

@@ -0,0 +1,76 @@
+<div itemscope itemtype="http://schema.org/Dataset">
+  <div itemscope itemprop="includedInDataCatalog" itemtype="http://schema.org/DataCatalog">
+    <meta itemprop="name" content="TensorFlow Datasets" />
+  </div>
+
+  <meta itemprop="name" content="esnli" />
+  <meta itemprop="description" content="&#10;The e-SNLI dataset extends the Stanford Natural Language Inference Dataset to&#10;include human-annotated natural language explanations of the entailment&#10;relations.&#10;&#10;&#10;To use this dataset:&#10;&#10;```python&#10;import tensorflow_datasets as tfds&#10;&#10;ds = tfds.load('esnli', 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/esnli" />
+  <meta itemprop="sameAs" content="https://github.com/OanaMariaCamburu/e-SNLI" />
+  <meta itemprop="citation" content="&#10;@incollection{NIPS2018_8163,&#10;title = {e-SNLI: Natural Language Inference with Natural Language Explanations},&#10;author = {Camburu, Oana-Maria and Rockt&quot;{a}schel, Tim and Lukasiewicz, Thomas and Blunsom, Phil},&#10;booktitle = {Advances in Neural Information Processing Systems 31},&#10;editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},&#10;pages = {9539--9549},&#10;year = {2018},&#10;publisher = {Curran Associates, Inc.},&#10;url = {http://papers.nips.cc/paper/8163-e-snli-natural-language-inference-with-natural-language-explanations.pdf}&#10;}&#10;" />
+</div>
+
+# `esnli`
+
+The e-SNLI dataset extends the Stanford Natural Language Inference Dataset to
+include human-annotated natural language explanations of the entailment
+relations.
+
+*   URL:
+    [https://github.com/OanaMariaCamburu/e-SNLI](https://github.com/OanaMariaCamburu/e-SNLI)
+*   `DatasetBuilder`:
+    [`tfds.text.esnli.Esnli`](https://github.com/tensorflow/datasets/tree/master/tensorflow_datasets/text/esnli.py)
+
+`esnli` is configured with `tfds.core.dataset_builder.BuilderConfig` and has the
+following configurations predefined (defaults to the first one):
+
+*   `plain_text` (`v0.0.1`) (`Size: 195.04 MiB`): Plain text import of e-SNLI
+
+## `esnli/plain_text`
+
+Plain text import of e-SNLI
+
+Versions:
+
+*   **`0.0.1`** (default):
+
+### Statistics
+
+Split      | Examples
+:--------- | -------:
+ALL        | 569,033
+TRAIN      | 549,367
+VALIDATION | 9,842
+TEST       | 9,824
+
+### Features
+
+```python
+FeaturesDict({
+    'explanation': Text(shape=(), dtype=tf.string),
+    'hypothesis': Text(shape=(), dtype=tf.string),
+    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=3),
+    'premise': Text(shape=(), dtype=tf.string),
+})
+```
+
+### Homepage
+
+*   [https://github.com/OanaMariaCamburu/e-SNLI](https://github.com/OanaMariaCamburu/e-SNLI)
+
+## Citation
+
+```
+@incollection{NIPS2018_8163,
+title = {e-SNLI: Natural Language Inference with Natural Language Explanations},
+author = {Camburu, Oana-Maria and Rockt"{a}schel, Tim and Lukasiewicz, Thomas and Blunsom, Phil},
+booktitle = {Advances in Neural Information Processing Systems 31},
+editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
+pages = {9539--9549},
+year = {2018},
+publisher = {Curran Associates, Inc.},
+url = {http://papers.nips.cc/paper/8163-e-snli-natural-language-inference-with-natural-language-explanations.pdf}
+}
+```
+
+--------------------------------------------------------------------------------