From 7c5ed85de3cfdb47a2faa4ac33eb791c2f06203f Mon Sep 17 00:00:00 2001
From: Matthias <matthias@vo.gt>
Date: Fri, 16 Oct 2020 16:44:06 +0200
Subject: [PATCH] Small style changes and fixes

---
 .../model_maker_text_classification.ipynb        | 16 +++++++---------
 1 file changed, 7 insertions(+), 9 deletions(-)

diff --git a/tensorflow/lite/g3doc/tutorials/model_maker_text_classification.ipynb b/tensorflow/lite/g3doc/tutorials/model_maker_text_classification.ipynb
index 43168e394f5..3bfe50e3ffe 100644
--- a/tensorflow/lite/g3doc/tutorials/model_maker_text_classification.ipynb
+++ b/tensorflow/lite/g3doc/tutorials/model_maker_text_classification.ipynb
@@ -518,7 +518,7 @@
       "source": [
         "## Choose a `model_spec` that Represents a Model for Text Classifier\n",
         "\n",
-        "Each `model_spec` object represents a specific model for the text classifier. TensorFlow Lite Model Maker currently supports [MobileBERT](https://arxiv.org/pdf/2004.02984.pdf), averaging word embeddings and [BERT-Base]((https://arxiv.org/pdf/1810.04805.pdf) models.\n",
+        "Each `model_spec` object represents a specific model for the text classifier. TensorFlow Lite Model Maker currently supports [MobileBERT](https://arxiv.org/pdf/2004.02984.pdf), averaging word embeddings and [BERT-Base](https://arxiv.org/pdf/1810.04805.pdf) models.\n",
         "\n",
         "Supported Model | Name of model_spec | Model Description\n",
         "--- | --- | ---\n",
@@ -548,7 +548,7 @@
       "source": [
         "## Load Input Data Specific to an On-device ML App\n",
         "\n",
-        "The [SST-2](https://nlp.stanford.edu/sentiment/index.html) (Stanford Sentiment Treebank) is one of the tasks in the [GLUE](https://gluebenchmark.com/) benchmark . It contains 67,349 movie reviews for training and 872 movie reviews for validation. The dataset has two classes: positive and negative movie reviews.\n",
+        "The [SST-2](https://nlp.stanford.edu/sentiment/index.html) (Stanford Sentiment Treebank) is one of the tasks in the [GLUE](https://gluebenchmark.com/) benchmark. It contains 67,349 movie reviews for training and 872 movie reviews for validation. The dataset has two classes: positive and negative movie reviews.\n",
         "\n",
         "Download the archived version of the dataset and extract it.\n"
       ]
@@ -669,9 +669,7 @@
       "source": [
         "## Evaluate the Customized Model\n",
         "\n",
-        "Evaluate the result of the model and get the loss and accuracy of the model.\n",
-        "\n",
-        "Evaluate the loss and accuracy in the test data."
+        "Evaluate the model with the test data and get its loss and accuracy.",
       ]
     },
     {
@@ -749,7 +747,7 @@
         "id": "HZKYthlVrTos"
       },
       "source": [
-        "You can evalute the tflite model with `evaluate_tflite` method."
+        "You can evalute the tflite model with `evaluate_tflite` method to get its accuracy."
       ]
     },
     {
@@ -760,7 +758,7 @@
       },
       "outputs": [],
       "source": [
-        "model.evaluate_tflite('average_word_vec/model.tflite', test_data)"
+        "accuracy = model.evaluate_tflite('average_word_vec/model.tflite', test_data)"
       ]
     },
     {
@@ -771,7 +769,7 @@
       "source": [
         "## Advanced Usage\n",
         "\n",
-        "The `create` function is the driver function that the Model Maker library uses to create models. The `model spec` parameter defines the model specification. The `AverageWordVecModelSpec` and `BertClassifierModelSpec` classes are currently supported. The `create` function comprises of the following steps:\n",
+        "The `create` function is the driver function that the Model Maker library uses to create models. The `model_spec` parameter defines the model specification. The `AverageWordVecModelSpec` and `BertClassifierModelSpec` classes are currently supported. The `create` function comprises of the following steps:\n",
         "\n",
         "1. Creates the model for the text classifier according to `model_spec`.\n",
         "2. Trains the classifier model.  The default epochs and the default batch size are set by the `default_training_epochs` and `default_batch_size` variables in the `model_spec` object.\n",
@@ -867,7 +865,7 @@
         "The model parameters you can adjust are:\n",
         "\n",
         "* `seq_len`: Length of the sequence to feed into the model.\n",
-        "* `initializer_range`: The standard deviation of the truncated_normal_initializer for initializing all weight matrices.\n",
+        "* `initializer_range`: The standard deviation of the `truncated_normal_initializer` for initializing all weight matrices.\n",
         "* `trainable`: Boolean that specifies whether the pre-trained layer is trainable.\n",
         "\n",
         "The training pipeline parameters you can adjust are:\n",