Workshop

This repository is intended for didactic purposes only.

The target is to build a bot (software agent) that "identifies the sentiment" of a conversation and embed it into a webapp.

During the workshop a github app (link provided by the lecturer) is installed on the repository to automatically evaluate a metric or a set of metrics on the models pushed to the forks.

Workflow:

• The repository is forked and the github app (link provided by the lecturer) is installed
• Introductory examples are presented
• A ML model is trained and stored in models/model.h5 (e.g. Example 2)
• A software agent in webpage/agent/wojak.py is embedded into a webapp (flask, webpage/sentiment.py). This agent uses the trained model to infer the sentiment of a conversation.

Webapp with the embedded agent using the ML model

Examples

• Example 0:
  • Basic example from scratch.
  • Take aways:
    • Model: Linear regression
    • Error, Loss, Cost, Objective
    • Gradient descent, step size, convergence
    • Inference
• Example 1:
  • Classic, crude sentiment analysis on the IMDB database
• Example 1.1:
  • A very basic word encoder
• Example 2:
  • Creating the bot model: Applying the example code to our target problem
• Example 3:
  • Test the bot model: Simple model evaluation
• Example 4:
  • Creating the bot: Using specialized hardware GPU/TPU

Running the webapp

More info on running flask apps

Linux

export FLASK_APP=webpage/sentiment.py
flask run --port 5001

Windows PowerShell:

$env:FLASK_APP = "webpage/sentiment.py"
flask run --port 5001

Description of additional content

These files are intended to aid and orchestrate the process and are not needed to be modified.

• evaluate_model.py:
  • Used within a container (see ./Dockerfile) to evaluate the model by the scoring engine
• data_dev:
  • development dataset, plaintext
• data_test:
  • testing dataset used by the scoring mechanism, encrypted
• data_train:
  • train dataset used to train the model
• libs:
  • Helping functions, e.g. nicer visualization in tensorboard or an example evaluation of the model
• models:
  • models/model.h5: main model evaluated by the scoring engine and used by the agent
  • .easeml.yml: used by the scoring engine, should remain untouched for this workshop. More info see CI @ Ease.ml
• wojac_test.ipynb:
  • Test the agent and model without the webapp
• webpage:
  • webapp and agent code

Sentiments

• NPC used as no sentiment/default

As per the dataset labels:

• Other
• Sad
• Happy
• Angry

Badgen.net

State of the evaluation engine on this repository (needs to be modified to reflect forks)