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)
Please see environment.yml for more details
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 |
Download the tfrecord format dataset from here.
Alternatively, You can use python tools/dataset/avazu.py
to prepare Avazu dataset yourself.
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.