xiaoya1220 / jarvis

Jarvis is a toolbox built on top of TensorFlow2.0 that allows developers and researchers to easily build neural networks in TensorFlow, particularly CTR models for large-scale advertising and recommendation scenarios.

Jarvis is a toolbox built on top of TensorFlow2.0 that allows developers and researchers to easily build neural networks in TensorFlow, particularly CTR models for large-scale advertising and recommendation scenarios. It provides the implementation of Meitu's FLEN model .

Note that Jarvis is still actively under development, so feedback and contributions are welcome. Feel free to submit your contributions as a pull request.

Jarvis features:

• Scalability: fast training on large-scale networks with tens of millions of sparse features
• Extensible: easily register new models and criteria.
• Supported tasks:
  • CTR prediction
  • Multi-task learning (coming)
  • online learning (todo)

Getting Started

Requirements and Installation

Please see environment.yml for more details

Usage

You can use python scripts/flen.py to run FLEN model on Avazu dataset.

Expected output:

Variant	AUC	Logloss
FLEN	0.7519	0.3944
FLEND	0.7528	0.3944

Avazu dataset

Download the tfrecord format dataset from here.
Alternatively, You can use python tools/dataset/avazu.py to prepare Avazu dataset yourself.

Customization

Implement Your Own Model

If you have a well-perform algorithm and are willing to implement it in our toolkit to help more people, you can create a pull request, detailed information can be found here.