Detect Sentiment with Transformers you own this product

intermediate Python (particularly pandas) • random variables from probability • experiments and events from probability
skills learned
conditional probability • fine-tuning a large language model • hyperparameter optimization • monitor training experiments
Nicole Königstein
1 week · 6-8 hours per week · INTERMEDIATE

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Finative, the environmental, social, and governance (ESG) analytics company you work for, analyzes a high volume of data using advanced natural language processing (NLP) techniques to provide its clients with valuable insights about their sustainability. Your CEO has concerns that some of the companies Finative analyzes may be greenwashing: spreading disinformation about their sustainability in order to appear more environmentally conscious than they actually are.

As a data scientist for Finative, your task is to validate your sustainability reports by creating and analyzing them. You’ll compute conditional probability with Bayes’ Theorem, by hand, to better understand your model’s performance through metrics such as recall and precision. You’ll learn an efficient way to prepare your data from different sources and merge it into one dataset, which you’ll use to prepare tweets. To successfully classify the tweets, you’ll use a pre-trained large language model and fine-tune it using the Hugging Face ecosystem as well as hyperopt and Ray Tune. You’ll use TensorBoard and Weights & Biases to analyze and track your experiments, and you’ll analyze the tweets to determine whether enough negative sentiment exists to indicate that the company you analyzed has been greenwashing its data.

This project is designed for learning purposes and is not a complete, production-ready application or solution.

project author

Nicole Koenigstein

Nicole Königstein currently works as data science and technology lead at impactvise, an ESG analytics company, and as a quantitative researcher and technology lead at Quantmate, an innovative FinTech startup that leverages alternative data as part of its predictive modeling strategy. She’s a regular speaker, sharing her expertise at conferences such as ODSC Europe. In addition, she teaches Python, machine learning, and deep learning, and holds workshops at conferences including the Women in Tech Global Conference.


This liveProject is for ML engineers, intermediate-level Python programmers, and early-stage data scientists who are familiar with the basics of probability. To begin these liveProjects you’ll need to be familiar with the following:

  • Intermediate Python (declaring variables, loops, branches, working with arrays)
  • How to use Jupyter Notebooks and Google Colab
  • Basic familiarity with NumPy (indexing arrays, array creation, and manipulation)
  • Basic familiarity with pandas (how to create and manipulate DataFrames)
  • Probability basics
  • Data science basics


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