update download script for new paths for base model

update README

contrib/clojure-package/examples/bert/README.md

@@ -95,10 +95,13 @@ Once you have these three files, you will be able to run this example without pr
 
 ## Fine-tuning Sentence Pair Classification with BERT
 
-This was based off of the great tutorial for in Gluon-NLP [https://gluon-nlp.mxnet.io/examples/sentence_embedding/bert.html](https://gluon-nlp.mxnet.io/examples/sentence_embedding/bert.html)
+This was based off of the great tutorial for in Gluon-NLP [https://gluon-nlp.mxnet.io/examples/sentence_embedding/bert.html](https://gluon-nlp.mxnet.io/examples/sentence_embedding/bert.html).
+
+We use the pre-trained BERT model that was exported from GluonNLP via the scripts/bert/staticbert/static_export_base.py. For convience, the model has been downloaded for you by running the get_bert_data.sh file in the root directory of this example.
 
 It will fine tune the base bert model for use in a classification task for 3 epochs.
 
+
 ### Setup Guide
 
 #### Step 1: Download the model
@@ -111,7 +114,11 @@ From the example directory:
 ./get_bert_data.sh
 ```
 
-Some sample questions and answers are provide in the `squad-sample.edn` file. Some are taken directly from the SQuAD dataset and one was just made up. Feel free to edit the file and add your own!
+### To run the notebook walkthrough
+
+There is a Jupyter notebook that uses the `lein juptyer` plugin to be able to execute Clojure code in project setting. The first time that you run it you will need to install the kernal with `lein jupyter install-kernel`. After that you can open the notebook in the project directory with `lein jupyter notebook`.
+
+There is also an exported copy of the walkthrough to markdown `fine-tune-bert.md`.
 
 
 ### To run

contrib/clojure-package/examples/bert/get_bert_data.sh

@@ -26,7 +26,7 @@ if [ ! -d "$data_path" ]; then
   curl https://s3.us-east-2.amazonaws.com/mxnet-scala/scala-example-ci/BertQA/vocab.json -o $data_path/vocab.json
   curl https://s3.us-east-2.amazonaws.com/mxnet-scala/scala-example-ci/BertQA/static_bert_qa-0002.params -o $data_path/static_bert_qa-0002.params
   curl https://s3.us-east-2.amazonaws.com/mxnet-scala/scala-example-ci/BertQA/static_bert_qa-symbol.json -o $data_path/static_bert_qa-symbol.json
-  curl https://media.githubusercontent.com/media/gigasquid/mxnet-data/master/static_bert_base_net-symbol.json -o $data_path/static_bert_base_net-symbol.json
-  curl https://media.githubusercontent.com/media/gigasquid/mxnet-data/master/static_bert_base_net-0000.params -o $data_path/static_bert_base_net-0000.params
+  curl https://s3.us-east-2.amazonaws.com/mxnet-scala/scala-example-ci/BertQA/static_bert_base_net-symbol.json -o $data_path/static_bert_base_net-symbol.json
+  curl https://s3.us-east-2.amazonaws.com/mxnet-scala/scala-example-ci/BertQA/static_bert_base_net-0000.params -o $data_path/static_bert_base_net-0000.params
   curl https://raw.githubusercontent.com/dmlc/gluon-nlp/master/docs/examples/sentence_embedding/dev.tsv -o $data_path/dev.tsv
 fi