Bert Classification Tutorial: Fine-tune BERT for text classification

Understand the project

Practical overview

Use it when learning or adapting a command-line BERT text-classification experiment, especially one based on the Japanese livedoor news corpus.

NLP learners seeking a code-based introduction to fine-tuning pretrained BERT.
Researchers or developers looking for a structured starting point for labeled text-classification experiments.
Readers interested in Japanese news categorization or adapting a BERT experiment template to another classification task.

Understand the core fine-tuning loop for a pretrained BERT classifier.
Prepare the livedoor news corpus for a nine-category classification task.
Separate training, development and test data for comparable experiments.
Use a documented Python and PyTorch project structure as a reference for another labeled text task.

Before you choose

The repository is public on GitHub and lists an MIT license.

This is a code repository and tutorial, not a hosted classifier or model API.
The worked dataset is the livedoor news corpus, with nine news categories.
The stated baseline stack is Python 3.10+, PyTorch 2.0 and Transformers 4.30 or later.
The README offers Poetry and Miniconda-based setup routes; GPU users must select a CUDA version suited to their environment.
Preprocessing is designed to be performed once, with fixed train, development and test splits supporting comparisons across experiments.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install the repository’s Python dependencies before following its data and training workflow.

Python 3.10 or later
Poetry 1.5.1 or later
A Python version-management tool such as pyenv, anyenv or asdf to install Python 3.10

From the repository root, run poetry install.
Use the repository’s documented poetry run python form for subsequent Python programs.

The project dependencies are installed in the Poetry-managed environment, ready for the documented workflow.

This guide covers environment setup only; it does not verify installation or model execution.
The README also describes a separate Miniconda and pip setup, with CUDA configuration depending on the execution environment.

Evidence and transparency

Source record details

Source availability: Active
Sources checked: 16 October 2026
Original website title: hppRC/bert-classification-tutorial · Official homepage title · retained in its source language