fix: update g3doc links

Author: emmanuel-ferdman

site/en/hub/common_saved_model_apis/images.md

@@ -70,7 +70,7 @@ consumer. The SavedModel itself should not perform dropout on the actual outputs
 Reusable SavedModels for image feature vectors are used in
 
 *   the Colab tutorial
-    [Retraining an Image Classifier](https://colab.research.google.com/github/tensorflow/docs/blob/master/g3doc/en/hub/tutorials/tf2_image_retraining.ipynb),
+    [Retraining an Image Classifier](https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/hub/tutorials/tf2_image_retraining.ipynb),
 
 <a name="classification"></a>
 

site/en/hub/common_saved_model_apis/text.md

@@ -94,7 +94,7 @@ distributed way. For example
 ### Examples
 
 *   Colab tutorial
-    [Text Classification with Movie Reviews](https://colab.research.google.com/github/tensorflow/docs/blob/master/g3doc/en/hub/tutorials/tf2_text_classification.ipynb).
+    [Text Classification with Movie Reviews](https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/hub/tutorials/tf2_text_classification.ipynb).
 
 <a name="text-embeddings-preprocessed"></a>
 

site/en/hub/installation.md

@@ -50,8 +50,8 @@ $ pip install --upgrade tf-hub-nightly
 
 -   [Library overview](lib_overview.md)
 -   Tutorials:
-    -   [Text classification](https://github.com/tensorflow/docs/blob/master/g3doc/en/hub/tutorials/tf2_text_classification.ipynb)
-    -   [Image classification](https://github.com/tensorflow/docs/blob/master/g3doc/en/hub/tutorials/tf2_image_retraining.ipynb)
+    -   [Text classification](https://github.com/tensorflow/docs/blob/master/site/en/hub/tutorials/tf2_text_classification.ipynb)
+    -   [Image classification](https://github.com/tensorflow/docs/blob/master/site/en/hub/tutorials/tf2_image_retraining.ipynb)
     -   Additional examples
         [on GitHub](https://github.com/tensorflow/hub/blob/master/examples/README.md)
 -   Find models on [tfhub.dev](https://tfhub.dev).

site/en/hub/migration_tf2.md

@@ -48,8 +48,8 @@ model = tf.keras.Sequential([
 
 Many tutorials show these APIs in action. See in particular
 
-*   [Text classification example notebook](https://github.com/tensorflow/docs/blob/master/g3doc/en/hub/tutorials/tf2_text_classification.ipynb)
-*   [Image classification example notebook](https://github.com/tensorflow/docs/blob/master/g3doc/en/hub/tutorials/tf2_image_retraining.ipynb)
+*   [Text classification example notebook](https://github.com/tensorflow/docs/blob/master/site/en/hub/tutorials/tf2_text_classification.ipynb)
+*   [Image classification example notebook](https://github.com/tensorflow/docs/blob/master/site/en/hub/tutorials/tf2_image_retraining.ipynb)
 
 ### Using the new API in Estimator training
 

site/en/hub/tf2_saved_model.md

@@ -51,7 +51,7 @@ model = tf.keras.Sequential([
 ```
 
 The [Text classification
-colab](https://colab.research.google.com/github/tensorflow/docs/blob/master/g3doc/en/hub/tutorials/tf2_text_classification.ipynb)
+colab](https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/hub/tutorials/tf2_text_classification.ipynb)
 is a complete example how to train and evaluate such a classifier.
 
 The model weights in a `hub.KerasLayer` are set to non-trainable by default.
@@ -244,7 +244,7 @@ to the Keras model, and runs the SavedModel's computation in training
 mode (think of dropout etc.).
 
 The [image classification
-colab](https://github.com/tensorflow/docs/blob/master/g3doc/en/hub/tutorials/tf2_image_retraining.ipynb)
+colab](https://github.com/tensorflow/docs/blob/master/site/en/hub/tutorials/tf2_image_retraining.ipynb)
 contains an end-to-end example with optional fine-tuning.
 
 #### Re-exporting the fine-tuning result

site/en/hub/tutorials/text_cookbook.md

@@ -34,7 +34,7 @@ library for tokenization and preprocessing.
 
 ### Kaggle
 
-[IMDB classification on Kaggle](https://github.com/tensorflow/docs/blob/master/g3doc/en/hub/tutorials/text_classification_with_tf_hub_on_kaggle.ipynb) -
+[IMDB classification on Kaggle](https://github.com/tensorflow/docs/blob/master/site/en/hub/tutorials/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.
 
@@ -43,14 +43,14 @@ downloading the data and submitting the results.
 [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/tutorials/keras/text_classification_with_hub)                                                                                |                                                                                                   | ![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/docs/blob/master/g3doc/en/hub/tutorials/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)
+[IMDB classification on Kaggle](https://github.com/tensorflow/docs/blob/master/site/en/hub/tutorials/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)
 
 ### Bangla task with FastText embeddings
 TensorFlow Hub does not currently offer a module in every language. The
 following tutorial shows how to leverage TensorFlow Hub for fast experimentation
 and modular ML development.
 
-[Bangla Article Classifier](https://github.com/tensorflow/docs/blob/master/g3doc/en/hub/tutorials/bangla_article_classifier.ipynb) -
+[Bangla Article Classifier](https://github.com/tensorflow/docs/blob/master/site/en/hub/tutorials/bangla_article_classifier.ipynb) -
 demonstrates how to create a reusable TensorFlow Hub text embedding, and use it
 to train a Keras classifier for
 [BARD Bangla Article dataset](https://github.com/tanvirfahim15/BARD-Bangla-Article-Classifier).
@@ -64,24 +64,24 @@ setup (no training examples).
 
 ### Basic
 
-[Semantic similarity](https://github.com/tensorflow/docs/blob/master/g3doc/en/hub/tutorials/semantic_similarity_with_tf_hub_universal_encoder.ipynb) -
+[Semantic similarity](https://github.com/tensorflow/docs/blob/master/site/en/hub/tutorials/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/docs/blob/master/g3doc/en/hub/tutorials/cross_lingual_similarity_with_tf_hub_multilingual_universal_encoder.ipynb) -
+[Cross-lingual semantic similarity](https://github.com/tensorflow/docs/blob/master/site/en/hub/tutorials/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/docs/blob/master/g3doc/en/hub/tutorials/retrieval_with_tf_hub_universal_encoder_qa.ipynb) -
+[Semantic retrieval](https://github.com/tensorflow/docs/blob/master/site/en/hub/tutorials/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/docs/blob/master/g3doc/en/hub/tutorials/semantic_similarity_with_tf_hub_universal_encoder_lite.ipynb) -
+[Semantic similarity with universal encoder lite](https://github.com/tensorflow/docs/blob/master/site/en/hub/tutorials/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.