Update /text/tutorials and /text/guide index pages to reflect updated…

… navigation.

PiperOrigin-RevId: 551324814

docs/guide/index.md

@@ -15,6 +15,14 @@ utilities. It's the recommended solution for most NLP use cases.
   complexity, from using a pre-trained model to building your own Transformer
   from scratch.
 
+## `tf.strings`
+
+The `tf.strings` module provides operations for working with string Tensors.
+
+* [Unicode strings](https://tensorflow.org/text/guide/unicode):
+  Represent Unicode strings in TensorFlow and manipulate them using Unicode
+  equivalents of standard string ops.
+
 ## TensorFlow Text
 
 If you need access to lower-level text processing tools, you can use TensorFlow
@@ -23,16 +31,6 @@ work with input in text form such as raw text strings or documents.
 
 * [Introduction to TensorFlow Text](https://tensorflow.org/text/guide/tf_text_intro):
   Learn how to install TensorFlow Text or build it from source.
-
-### Concepts
-
-* [Unicode strings](https://tensorflow.org/text/guide/unicode):
-  Represent Unicode strings in TensorFlow and manipulate them using Unicode
-  equivalents of standard string ops.
-* [Word embeddings](https://tensorflow.org/text/guide/word_embeddings):
-  Train your own word embeddings using a simple Keras model for a sentiment
-  classification task, and then visualize them using the
-  [Embedding Projector](https://www.tensorflow.org/tensorboard/tensorboard_projector_plugin).
 * [Converting TensorFlow Text operators to TensorFlow Lite](https://tensorflow.org/text/guide/text_tf_lite):
   Convert a TensorFlow Text model to TensorFlow Lite for deployment to mobile,
   embedded, and IoT devices.
@@ -55,7 +53,7 @@ work with input in text form such as raw text strings or documents.
 
 The TensorFlow Models - NLP library provides Keras primitives that can be
 assembled into Transformer-based models, and scaffold classes that enable easy
-experimentation with novel achitectures.
+experimentation with novel architectures.
 
 * [Introduction to the TensorFlow Models NLP library](https://tensorflow.org/tfmodels/nlp):
   Build Transformer-based models for common NLP tasks including pre-training,

docs/tutorials/index.md

@@ -30,6 +30,9 @@ help you work with input in text form such as raw text strings or documents.
 * [Neural machine translation with a Transformer and Keras](https://tensorflow.org/text/tutorials/transformer):
   Create and train a sequence-to-sequence Transformer model to translate
   Portuguese into English.
+* [Image captioning with visual attention](https://tensorflow.org/text/tutorials/image_captioning):
+  Generate image captions using a Transformer-decoder model built with attention
+  layers.
 
 ## Text classification
 
@@ -54,4 +57,16 @@ help you work with input in text form such as raw text strings or documents.
 * [Uncertainty-aware Deep Language Learning with BERT-SNGP](https://tensorflow.org/text/tutorials/uncertainty_quantification_with_sngp_bert):
   Apply [SNGP](https://arxiv.org/abs/2006.10108) to a natural language
   understanding (NLU) task. Building on a BERT encoder, you'll improve the NLU
-  model's ability to detect out-of-scope queries.
+  model's ability to detect out-of-scope queries.
+
+## Embeddings
+
+* [Word embeddings](https://tensorflow.org/text/guide/word_embeddings):
+  Train your own word embeddings using a simple Keras model for a sentiment
+  classification task, and then visualize them using the
+  [Embedding Projector](https://www.tensorflow.org/tensorboard/tensorboard_projector_plugin).
+* [Warm-start embedding layer matrix](https://tensorflow.org/text/tutorials/warmstart_embedding_matrix):
+  Learn how to "warm-start" training for a text sentiment classification model.
+* [word2vec](https://tensorflow.org/text/tutorials/word2vec): Train a word2vec
+  model on a small dataset and visualize the trained embeddings in the
+  [Embedding Projector](https://www.tensorflow.org/tensorboard/tensorboard_projector_plugin).