Add the "text cookbook": a catalog of available Hub resources in the …

…text domain.

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docs/tutorials/text_cookbook.md

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+# Text Cookbook
+
+This page lists a set of known guides and tools solving problems in the text
+domain with TensorFlow Hub. It is a starting place for anybody who wants to
+solve typical ML problems using pre-trained ML components rather than starting
+from scratch.
+
+[TOC]
+
+## Classification tasks
+
+When we want to predict a class for a given example, for example **sentiment**,
+**toxicity**, **article category**, or any other characteristic.
+
+![Text Classification Graphic](https://www.gstatic.com/aihub/tfhub/universal-sentence-encoder/example-classification.png){height="150"}
+
+The tutorials below are solving the same task from different perspectives and
+using different tools.
+
+### Estimator
+
+[Text classification](https://www.tensorflow.org/hub/tutorials/text_classification_with_tf_hub) -
+example for building an IMDB sentiment classifier with Estimator. Contains
+multiple tips for improvement and a module comparison section.
+
+### Keras
+
+[Text classification with Keras](https://www.tensorflow.org/beta/tutorials/keras/basic_text_classification_with_tfhub) -
+example for building an IMDB sentiment classifier with Keras and TensorFlow
+Datasets.
+
+
+### BERT
+[Predicting Movie Review Sentiment with BERT on TF Hub](https://github.com/google-research/bert/blob/master/predicting_movie_reviews_with_bert_on_tf_hub.ipynb) -
+shows how to use a BERT module for classification. Includes use of `bert`
+library for tokenization and preprocessing.
+
+### Kaggle
+[IMDB classification on Kaggle](https://github.com/tensorflow/hub/blob/master/examples/colab/text_classification_with_tf_hub_on_kaggle.ipynb) - shows how to easily interact with a Kaggle competition from a Colab,
+including downloading the data and submitting the results.
+
+
+|           | Estimator | Keras | TF2 | TF Datasets | BERT | Kaggle APIs |
+| --------- | --------- | ----- | --- | ----------- | ---- | ----------- |
+| [Text classification](https://www.tensorflow.org/hub/tutorials/text_classification_with_tf_hub) | ![done](https://www.gstatic.com/images/icons/material/system_gm/1x/bigtop_done_googblue_18dp.png) | | | | | |
+| [Text classification with Keras](https://www.tensorflow.org/beta/tutorials/keras/basic_text_classification_with_tfhub)| | ![done](https://www.gstatic.com/images/icons/material/system_gm/1x/bigtop_done_googblue_18dp.png) | ![done](https://www.gstatic.com/images/icons/material/system_gm/1x/bigtop_done_googblue_18dp.png) | ![done](https://www.gstatic.com/images/icons/material/system_gm/1x/bigtop_done_googblue_18dp.png) | | |
+| [Predicting Movie Review Sentiment with BERT on TF Hub](https://github.com/google-research/bert/blob/master/predicting_movie_reviews_with_bert_on_tf_hub.ipynb)| ![done](https://www.gstatic.com/images/icons/material/system_gm/1x/bigtop_done_googblue_18dp.png) | | | | ![done](https://www.gstatic.com/images/icons/material/system_gm/1x/bigtop_done_googblue_18dp.png) | |
+| [IMDB classification on Kaggle](https://github.com/tensorflow/hub/blob/master/examples/colab/text_classification_with_tf_hub_on_kaggle.ipynb)| ![done](https://www.gstatic.com/images/icons/material/system_gm/1x/bigtop_done_googblue_18dp.png) | | | | | ![done](https://www.gstatic.com/images/icons/material/system_gm/1x/bigtop_done_googblue_18dp.png) |
+
+
+## Semantic similarity
+
+When we want to find out which sentences correlate with each other in zero-shot
+setup (no training examples).
+
+![Semantic Similarity Graphic](https://www.gstatic.com/aihub/tfhub/universal-sentence-encoder/example-similarity.png){height="150"}
+
+### Basic
+[Semantic similarity](https://github.com/tensorflow/hub/blob/master/examples/colab/semantic_similarity_with_tf_hub_universal_encoder.ipynb) - shows how to use the sentence encoder module to compute sentence
+similarity.
+
+### Cross-lingual
+[Cross-lingual semantic similarity](https://github.com/tensorflow/hub/blob/master/examples/colab/cross_lingual_similarity_with_tf_hub_multilingual_universal_encoder.ipynb) - shows how to use one of the cross-lingual sentence
+encoders to compute sentence similarity across languages.
+
+### Semantic retrieval
+[Semantic retrieval](https://github.com/tensorflow/hub/blob/master/examples/colab/retrieval_with_tf_hub_universal_encoder_qa.ipynb) - shows how to use Q/A sentence encoder to index a collection of documents
+for retrieval based on semantic similarity.
+
+### SentencePiece input
+[Semantic similarity with universal encoder lite](https://github.com/tensorflow/hub/blob/master/examples/colab/semantic_similarity_with_tf_hub_universal_encoder_lite.ipynb) - shows how to use sentence encoder modules that accept
+[SentencePiece](https://github.com/google/sentencepiece) ids on input instead of
+text.
+
+
+## Module creation
+Instead of using only modules on [tfhub.dev](https://tfhub.dev), there are ways
+to create own modules. This can be a useful tool for better ML codebase
+modularity and for further sharing.
+
+### Wrapping existing pre-trained embeddings
+[Text embedding module exporter](https://github.com/tensorflow/hub/blob/master/examples/text_embeddings/export.py) -
+a tool to wrap an existing pre-trained embedding into a module. Shows how to
+include text pre-processing ops into the module. This allows to create a
+sentence embedding module from token embeddings.
+
+[Text embedding module exporter v2](https://github.com/tensorflow/hub/blob/master/examples/text_embeddings_v2/export_v2.py) -
+same as above, but compatible with TensorFlow 2 and eager execution.
+
+### Create trainable RNN module
+[RNN model exporter](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/saved_model/integration_tests/export_text_rnn_model.py) -
+shows how to create an uninitialized trainable LSTM based module compatible with
+TensorFlow 2. The module exposes two signatures, one for training by directly
+feeding in sentences, the other for decoding - constructing a statistically most
+likely sentence.