diff --git a/site/en/hub/common_saved_model_apis/images.md b/site/en/hub/common_saved_model_apis/images.md index 9754d52feed..5413f0adc07 100644 --- a/site/en/hub/common_saved_model_apis/images.md +++ b/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), diff --git a/site/en/hub/common_saved_model_apis/text.md b/site/en/hub/common_saved_model_apis/text.md index c618b02d9f1..209319f27a9 100644 --- a/site/en/hub/common_saved_model_apis/text.md +++ b/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). diff --git a/site/en/hub/installation.md b/site/en/hub/installation.md index 33594cd3079..2381fbea614 100644 --- a/site/en/hub/installation.md +++ b/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). \ No newline at end of file diff --git a/site/en/hub/migration_tf2.md b/site/en/hub/migration_tf2.md index 24c1bf14c4d..c2cc4b50759 100644 --- a/site/en/hub/migration_tf2.md +++ b/site/en/hub/migration_tf2.md @@ -46,10 +46,10 @@ model = tf.keras.Sequential([ ...]) ``` -Many tutorials show these APIs in action. See in particular +Many tutorials show these APIs in action. Here are some examples: -* [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 diff --git a/site/en/hub/tf2_saved_model.md b/site/en/hub/tf2_saved_model.md index 641f9b3517b..e41337b2548 100644 --- a/site/en/hub/tf2_saved_model.md +++ b/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 diff --git a/site/en/hub/tutorials/text_cookbook.md b/site/en/hub/tutorials/text_cookbook.md index 0ac9c6d6df3..dee9c1cf466 100644 --- a/site/en/hub/tutorials/text_cookbook.md +++ b/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.