“Machine learning can help you understand and predict intent in ways that simply aren’t possible manually. It can also help you find missed or unexpected connections between business goals and the habits of your key customer segments.”

1. Query Classification
on Steroids
with BERT
Hamlet Batista
@hamletbatista

2. https://bit.ly/2UVebc1

3. https://bit.ly/2UVebc1

4. https://bit.ly/2UVebc1

5. “Machine learning can help you
understand and predict intent in
ways that simply aren’t possible
manually. It can also help you find
missed or unexpected connections
between business goals and the
habits of your key customer
segments.”
https://bit.ly/2UVebc1

7. Agenda • We will build a sophisticated deep
learning model that can parse not just
intent but also specific actions like the
ones you give to Google Assistant, Siri and
Alexa.
• We will access the predictive model using
a simple custom function in Google
Sheets.
• We will review the concepts that make this
possible.
• I will share resources you can use to learn
more.

8. Our Toolkit
https://colab.research.google.com/
We will type our
commands in a
Google Colab
notebook.

9. Our Toolkit
https://uber.github.io/ludwig/
Ludwig will
help us build
our deep
learning model
without writing
code.

10. Our Toolkit
https://github.com/howl-anderson/ATIS_dataset
We will train our
model using
the ATIS dataset.

11. Our Toolkit
https://ngrok.com/
Ngrok will help
us expose the
service URL
inside our
notebook.

12. Our Toolkit
https://developers.google.com/apps-script
We will code
our Google
Sheets function
using Apps
Script.

13. Setting up the
notebook
Downloading all required packages

14. Our Empty Notebook
https://bit.ly/39Fkm9r

16. Installing
Tensorflow
%tensorflow_version
1.x
!pip install tensorflow-
gpu==1.15.2
import tensorflow as tf;
print(tf.__version__)

17. !pip install ludwig
!pip install
ludwig[serve]
Installing
Ludwig

18. https://www.kaggle.com/siddhadev/atis-dataset-clean/home

19. Connect
Google Drive
from google.colab
import drive
drive.mount("/bert")
%cd '/bert/My
Drive/Writing/Query
Classification on
Steroids with BERT'

20. !wget
https://bin.equinox.io/
c/4VmDzA7iaHb/ngrok-
stable-linux-amd64.zip
&& unzip ngrok-stable-
linux-amd64.zip
Installing
Ngrok

21. Preparing the
training data
Setting up the Ludwig configuration file

23. Creating
model_definition.
yaml
https://gist.github.com/hamletbatista/b523424bb854372288b
b2de325291a13

24. Building the
predictive model
Running Ludwig with our
configuration file

25. !ludwig experiment
--data_csv
atis.train.csv
--
model_definition_file
model_definition.yaml
Running
Ludwig

27. Test the predictive
model
Running Ludwig serve

28. Python code
snippet to test
the model
import pandas as pd
test_df = pd.read_csv("atis.test.csv",
index_col=0)
model =
LudwigModel.load("results/experiment_r
un/model")
predictions = model.predict(test_df)
test_df.reset_index().join(predictions)[["to
kens", "intent_predictions",
"slots_predictions"]]

30. Serving the
predictive model
Running Ludwig serve

31. %%bash --bg
nohup ludwig serve -m
results/experiment_run
/model > debug.log
2>&1
!tail debug.log
Running
Ludwig serve

33. !curl
http://0.0.0.0:8000/pre
dict -X POST -F
'tokens=BOS give me a
flight from baltimore to
newark EOS'
Testing the
service

34. %%bash --bg
./ngrok http 8000 2>
ngrok.log
!tail ngrok.log
Running
Ngrok

35. !curl -s
http://localhost:4040/a
pi/tunnels | python3 -c
"import sys, json;
print(json.load(sys.stdi
n)['tunnels'][0]['public_
url'])"
Extracting the
Ngrok public
URL

36. !curl
https://e769db6e.ngro
k.io/predict -X POST -F
'tokens=BOS explain
meal codes sd d EOS'
Testing the
public service

37. Our journey
so far
1.We built a travel specific
intent prediction model
2.We can access the model
using a temporary public URL
3.We need to access the URL
from Apps Script and Google
Sheets

38. Accessing the
predictive model
from Apps Script
Creating a Google Sheets Custom
Function

40. Creating
fetchPrediction()
https://gist.github.com/hamletbatista/fb7eae2e846ee8c43fff5
a433df2c672

41. Test
fetchPre
diction()

44. Testing in Google
Sheets
Calling our custom function
fetchPrediction

45. Testing in
Google Sheets

47. Testing in Google Sheets

48. Where is BERT?

49. Creating a BERT
model_definition.
yaml
https://gist.github.com/hamletbatista/4d8e6d881b47c1d6b8d
a7ce2cc494579

50. BERT performs worse!

51. Resources to
learn more

52. Python and deep learning resources
1. https://www.searchenginejournal.c
om/author/hamlet-batista/
1. Automated Intent Classification Using
Deep Learning in Google Sheets
2. Automated Intent Classification Using
Deep Learning
3. Automated Intent Classification Using
Deep Learning (Part 2)
2. https://www.jcchouinard.com/pyth
on-for-seo/
1. Python for SEO: Complete Guide (in 5
Chapters)
3. https://moz.com/blog/intro-to-
python
1. Intro to Python - Whiteboard Friday

53. Thank You!
@hamletbatista
www.ranksense.com/free-tools/
www.linkedin.com/in/
hamletbatista/