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Bertclassifier

Bertclassifier is an MIT-licensed Python project that uses PyTorch and Hugging Face Transformers to train and run a BERT-based Chinese text classifier. It includes CNews training and validation data workflow instructions, plus scripts for training and prediction.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Bertclassifier — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it as a hands-on baseline when you want to inspect or run a simple supervised Chinese text-classification workflow built around BERT.

Who it is for

  • Developers learning how BERT-based text classification is structured and trained.
  • Practitioners experimenting with supervised Chinese text categorization.

Documented uses

  • Train a classifier on the project's CNews category workflow.
  • Run predictions on Chinese text with the included prediction script.
  • Explore adapting the classifier head and training examples for a different set of Chinese labels.
Before you choose

Requirements & limits

The repository is public and lists an MIT license.

  • The README describes a specific CNews workflow with 50,000 training examples in 10 categories and 5,000 validation examples.
  • The project reports 0.92 best validation accuracy for that setup; results for other data are not specified.
  • The documented stack includes Python, PyTorch and Transformers, with example versions and hardware listed in the README.

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

Set up and run the documented CNews workflow

Prepare the example data and dependencies, then run the repository's training and prediction scripts.

Before you start

  • The Bertclassifier repository.
  • The CNews data described by the README, placed in a data folder.
  • A models folder to save model files.
  • Dependencies installed from requirements.txt.

Steps

  1. Create a data folder and place the downloaded CNews data in it.

  2. Create a models folder for model files.

  3. Install the listed dependencies with pip install -r requirements.txt.

  4. Run python train.py, followed by python predict.py.

Expected outcome

The documented workflow provides scripts to train the classifier and run predictions.

Guide limitations

  • The README lists Ubuntu 18.04, CUDA 10.2, PyTorch 1.6.0 and Transformers 3.2.0 as its example environment; it does not establish compatibility with other versions.
  • The reported validation accuracy of 0.92 applies to the project’s described dataset and setup.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
16 October 2026
Original website title
illiterate/BertClassifier · Official homepage title · retained in its source language
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