message: Small style changes and fixes
diff: "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",
  "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.",
  ]
  },
  {
  "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."
  ]
  },
  {
  },
  "outputs": [],
  "source": [
  - "model.evaluate_tflite('average_word_vec/model.tflite', test_data)"
  + "accuracy = model.evaluate_tflite('average_word_vec/model.tflite', test_data)"
  ]
  },
  {
  "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",
  "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",
  