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This question is commonly asked for Machine Learning Engineer candidates at OpenAI during technical interviews.

Debug transformer and train classifier | OpenAI Interview Question

Quick Overview

This question evaluates debugging and practical implementation skills for transformer-based text classification, covering model correctness, training loop integrity, data preprocessing, and evaluation metrics within the Machine Learning / Natural Language Processing domain.

Debug and Fix a Transformer Text Classifier, Then Train and Evaluate It

Context

You inherit a small codebase for a transformer-based text classifier. There are four failing unit tests: two correspond to previously documented ("known") issues; two are unexpected ("novel"). Your task is to make the model train and evaluate correctly, and to demonstrate a robust training/evaluation pipeline on a labeled dataset.

Assumptions (to make the task self-contained):

Language: Python 3.10+
Libraries: PyTorch, Hugging Face Transformers, scikit-learn, pandas, numpy
Dataset: a CSV file with columns text (string) and label (int), single-label classification with K classes.

Tasks

Identify the root cause of each failing test (2 known bugs, 2 novel bugs), and fix the model/training code so all tests pass.
Provide a clean, minimal reference implementation of the model and training loop that avoids these bugs.
Given a labeled dataset, analyze class balance and basic feature distributions (e.g., text length, token frequency), then train the classifier.
Report key performance metrics (accuracy, precision, recall, F1; ROC-AUC when binary), and include guardrails for class imbalance and reproducibility.

Quick Overview

Context

Assumptions (to make the task self-contained):

Language: Python 3.10+

Libraries: PyTorch, Hugging Face Transformers, scikit-learn, pandas, numpy

Dataset: a CSV file with columns text (string) and label (int), single-label classification with K classes.

Tasks

Identify the root cause of each failing test (2 known bugs, 2 novel bugs), and fix the model/training code so all tests pass.

Provide a clean, minimal reference implementation of the model and training loop that avoids these bugs.

Given a labeled dataset, analyze class balance and basic feature distributions (e.g., text length, token frequency), then train the classifier.

Report key performance metrics (accuracy, precision, recall, F1; ROC-AUC when binary), and include guardrails for class imbalance and reproducibility.

Debug transformer and train classifier

Quick Overview

Debug and Fix a Transformer Text Classifier, Then Train and Evaluate It

Context

Tasks

Solution

Comments (0)

Debug transformer and train classifier

Quick Overview

Debug and Fix a Transformer Text Classifier, Then Train and Evaluate It

Context

Tasks

Solution

Comments (0)