address doc comments

scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/infer/bert/BertDataParser.java

@@ -25,14 +25,19 @@
 import com.google.gson.JsonElement;
 import com.google.gson.JsonObject;
 
+/**
+ * This is the Utility for pre-processing the data for Bert Model
+ * You can use this utility to parse Vocabulary JSON into Java Array and Dictionary,
+ * clean and tokenize sentences and pad the text
+ */
 public class BertDataParser {
 
  private Map<String, Integer> token2idx;
  private List<String> idx2token;
 
  /**
  * Parse the Vocabulary to JSON files
- * [PAD], [CLS], [SEP], [MASK], [UNK] are reserved token
+ * [PAD], [CLS], [SEP], [MASK], [UNK] are reserved tokens
  * @param jsonFile the filePath of the vocab.json
  * @throws Exception
  */
@@ -52,13 +57,13 @@ void parseJSON(String jsonFile) throws Exception {
  }
 
  /**
- * Tokenize the input, split all kinds of spaces and
- * saparate the end of sentence symbol: . , ? !
- * @param input The input String
+ * Tokenize the input, split all kinds of whitespace and
+ * Separate the end of sentence symbol: . , ? !
+ * @param input The input string
  * @return List of tokens
  */
  List<String> tokenizer(String input) {
- String[] step1 = input.split("[\n\r\t ]+");
+ String[] step1 = input.split("\\s+");
  List<String> finalResult = new LinkedList<>();
  for (String item : step1) {
  if (item.length() != 0) {

scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/infer/bert/BertQA.java

@@ -26,6 +26,11 @@
 
 import java.util.*;
 
+/**
+ * This is an example of using BERT to do the general Question and Answer inference jobs
+ * Users can provide a question with a paragraph contains answer to the model and
+ * the model will be able to find the best answer from the answer paragraph
+ */
 public class BertQA {
  @Option(name = "--model-path-prefix", usage = "input model directory and prefix of the model")
  private String modelPathPrefix = "/model/static_bert_qa";

scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/infer/bert/README.md

@@ -17,16 +17,18 @@
 
 # Run BERT QA model using Java Inference API
 
-In this tutorial, we will walk through the BERT QA model trained by MXNet.
-You will be able to run inference with general Q & A task:
+In this tutorial, we will walk through the BERT QA model trained by MXNet. 
+Users can provide a question with a paragraph contains answer to the model and
+the model will be able to find the best answer from the answer paragraph.
 
+Example:
 ```text
 Q: When did BBC Japan start broadcasting?
 ```
 
-The model are expected to find the right answer in the corresponding text:
+Answer paragraph
 ```text
-BBC Japan was a general entertainment Channel. Which operated between December 2004 and April 2006.
+BBC Japan was a general entertainment channel, which operated between December 2004 and April 2006.
 It ceased operations after its Japanese distributor folded.
 ```
 And it picked up the right one:
@@ -74,18 +76,18 @@ From the `scala-package/examples/scripts/infer/bert/` folder run:
 
 ## Background
 
-To learn more about how BERT works in MXNet, please follow this [tutorial](https://medium.com/apache-mxnet/gluon-nlp-bert-6a489bdd3340).
+To learn more about how BERT works in MXNet, please follow this [MXNet Gluon tutorial on NLP using BERT](https://medium.com/apache-mxnet/gluon-nlp-bert-6a489bdd3340).
 
-The model was extracted from the GluonNLP with static length settings.
+The model was extracted from MXNet GluonNLP with static length settings.
 
 [Download link for the script](https://gluon-nlp.mxnet.io/_downloads/bert.zip)
 
-The original description can be found in [here](https://gluon-nlp.mxnet.io/model_zoo/bert/index.html#bert-base-on-squad-1-1).
+The original description can be found in the [MXNet GluonNLP model zoo](https://gluon-nlp.mxnet.io/model_zoo/bert/index.html#bert-base-on-squad-1-1).
 ```bash
 python static_finetune_squad.py --optimizer adam --accumulate 2 --batch_size 6 --lr 3e-5 --epochs 2 --gpu 0 --export
 
 ```
-This script would generate a `json` and `param` which are the standard MXNet model files.
+This script will generate `json` and `param` fles that are the standard MXNet model files.
 By default, this model are using `bert_12_768_12` model with extra layers for QA jobs.
 
 After that, to be able to use it in Java, we need to export the dictionary from the script to parse the text
@@ -97,5 +99,5 @@ f = open("vocab.json", "w")
 f.write(json_str)
 f.close()
 ```
-This would export a json file for you to deal with the vocabulary.
+This would export the token vocabulary in json format.
 Once you have these three files, you will be able to run this example without problems.

scala-package/examples/src/test/java/org/apache/mxnetexamples/javaapi/infer/predictor/BertExampleTest.java

@@ -26,6 +26,9 @@
 
 import java.io.File;
 
+/**
+ * Test on BERT QA model
+ */
 public class BertExampleTest {
  final static Logger logger = LoggerFactory.getLogger(BertExampleTest.class);
  private static String modelPathPrefix = "";