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Character Based Cnn

Character Based CNN is a Python repository containing a PyTorch character-level convolutional neural network implementation for text classification, with scripts to train a model from CSV data and run prediction using a saved model.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Character Based Cnn — 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 when you want to explore character-level text classification or adapt a PyTorch CNN implementation to a labeled dataset with binary targets.

Who it is for

  • Developers studying or adapting a character-level CNN for NLP.
  • Practitioners experimenting with binary text classification on labeled CSV data.

Documented uses

  • Train a character-based text classifier from labeled CSV rows.
  • Run prediction on input text using a saved model.
  • Explore large and small configurations of a character-level CNN.
Before you choose

Requirements & limits

The repository is public and displays an MIT license.

  • The documented training code supports binary labels only.
  • The configuration includes the character alphabet and maximum document length, which should be considered when adapting to different text or languages.
  • The README lists PyTorch 0.4.1 and additional Python dependencies, including TensorFlow for TensorBoardX.

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

Train a binary text classifier from CSV

Use the documented training script with a labeled CSV dataset.

Before you start

  • Python project dependencies listed in the README: NumPy, pandas, scikit-learn, PyTorch 0.4.1, TensorBoardX and TensorFlow.
  • A CSV file containing a text column and a label column, with binary labels 0 and 1.

Steps

  1. Set the dataset path and text and label column names in the training arguments.

  2. Configure options such as validation split, character alphabet, maximum document length, epochs and batch size as needed.

  3. Run the documented training command: python train.py --data_path=/data/tweets.csv --max_rows=200000.

Expected outcome

The training script is documented to train a text classifier and save model checkpoints when checkpointing is enabled.

Guide limitations

  • The README says the code currently works only with binary labels (0/1).
  • The example command uses a placeholder dataset path; the project does not provide that dataset in the supplied documentation.
Documentation 1 ↗
Source-based practical guide

Prepare to run prediction with a saved model

Identify the documented inputs required by the prediction script.

Before you start

  • A pre-trained model file.
  • Input text and any character alphabet or preprocessing settings needed for the model.

Steps

  1. Provide the saved model path and input text to predict.py.

  2. Set the preprocessing steps, alphabet, character count and maximum length to the values appropriate for the model.

Expected outcome

The README specifies the inputs for invoking prediction and inference with a saved model.

Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
17 October 2026
Original website title
ahmedbesbes/character-based-cnn · Official homepage title · retained in its source language
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