Crepe

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Crepe is an open-source coding & development tool that provides character-level convolutional networks for text classification. It is implemented in Torch 7 and can be used to reproduce research results.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is Crepe?

Crepe offers a robust implementation of character-level convolutional networks for text classification, built on Torch 7. This open-source project allows users to reproduce the experimental results from the "Character-level Convolutional Networks for Text Classification" article published in NIPS 2015. It includes data preprocessing scripts to convert CSV datasets into a Torch 7 binary format and a training program. The tool is designed for technical users and researchers, providing a foundation for advanced text classification tasks. While it requires a specific environment, including Torch 7 and potentially a powerful GPU, it serves as a valuable resource for understanding and applying character-level CNNs.

Best used for

Ideal for developers and data scientists who need to implement character-level convolutional networks, reproduce academic research in text classification, and build custom text analysis models. Especially valuable for those working with Torch 7 and seeking a foundational open-source implementation.

Common actions

classify text

train models

process data

reproduce research

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Capabilities

Key features

Character-level CNNs
Torch 7 implementation
Data preprocessing scripts
Training program
Reproduce research results

Target Audience

developerdata scientist

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What are the system requirements for running Crepe?

To run Crepe, you will need Torch 7 installed, along with packages like qtlua and qttorch for visualization. For optimal performance, especially with the provided examples, an NVidia GPU with at least 3GB of memory is recommended. The code is primarily in Lua.

Can Crepe be used for custom text classification datasets?

Yes, Crepe can be adapted for custom datasets. It includes data preprocessing scripts to convert CSV formatted data into the Torch 7 binary format required for training. You would also need to configure the model in `train/config.lua` to match the number of classes in your dataset.

How does Crepe compare to other text classification methods?

Crepe implements character-level convolutional networks, which are effective for text classification by learning features directly from characters. The original paper demonstrated strong performance. While the provided code is in Torch 7, an example implementation in NVIDIA DIGITS using CuDNN is noted to be significantly faster, suggesting potential for performance optimization with modern frameworks.

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AI Agents & Automation › AI Frameworks & Infra Research & Education › Scientific Computing

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