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A flexible package for multimodal-deep-learning to combine tabular data with text and images using Wide and Deep models in Pytorch
| Date | Stars |
|---|---|
| 2026-07-24 | 1417 |
| 2026-07-25 | 1417 |
| 2026-07-28 | 1417 |
| 2026-07-30 | 1417 |
| 2026-07-31 | 1417 |
| 2026-08-06 | 1418 |
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<p align="center">
<img width="300" src="mkdocs/sources/docs/figures/widedeep_logo.png">
</p>
[](https://pypi.org/project/pytorch-widedeep/)
[](https://pypi.org/project/pytorch-widedeep/)
[](https://github.com/jrzaurin/pytorch-widedeep/actions)
[](https://pytorch-widedeep.readthedocs.io/en/latest/?badge=latest)
[](https://codecov.io/gh/jrzaurin/pytorch-widedeep)
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[](https://github.com/jrzaurin/pytorch-widedeep/graphs/commit-activity)
[](https://github.com/jrzaurin/pytorch-widedeep/issues)
[](https://join.slack.com/t/pytorch-widedeep/shared_invite/zt-soss7stf-iXpVuLeKZz8lGTnxxtHtTw)
[](https://doi.org/10.21105/joss.05027)
# pytorch-widedeep
A flexible package for multimodal-deep-learning to combine tabular data with
text and images using Wide and Deep models in Pytorch
**Documentation:** [https://pytorch-widedeep.readthedocs.io](https://pytorch-widedeep.readthedocs.io/en/latest/index.html)
**Companion posts and tutorials:** [infinitoml](https://jrzaurin.github.io/infinitoml/)
**Experiments and comparison with `LightGBM`**: [TabularDL vs LightGBM](https://github.com/jrzaurin/tabulardl-benchmark)
**Slack**: if you want to contribute or just want to chat with us, join [slack](https://join.slack.com/t/pytorch-widedeep/shared_invite/zt-soss7stf-iXpVuLeKZz8lGTnxxtHtTw)
The content of this document is organized as follows:
- [pytorch-widedeep](#pytorch-widedeep)
- [Introduction](#introduction)
- [Architectures](#architectures)
- [The ``deeptabular`` component](#the-deeptabular-component)
- [The ``rec`` module](#the-rec-module)
- [Text and Images](#text-and-images)
- [Installation](#installation)
- [Developer Install](#developer-install)
- [Quick start](#quick-start)
- [Testing](#testing)
- [How to Contribute](#how-to-contribute)
- [Acknowledgments](#acknowledgments)
- [License](#license)
- [Cite](#cite)
- [BibTex](#bibtex)
- [APA](#apa)
### Introduction
``pytorch-widedeep`` is based on Google's [Wide and Deep Algorithm](https://arxiv.org/abs/1606.07792),
adjusted for multi-modal datasets.
In general terms, `pytorch-widedeep` is a package to use deep learning with
tabular data. In particular, is intended to facilitate the combination of
text and images with corresponding tabular data using wide and deep models.
With that in mind there are a number of architectures that can be implemented
with the library. The main components of those architectures are shown in the
Figure below:
<p align="center">
<img width="750" src="mkdocs/sources/docs/figures/widedeep_arch_new.png">
</p>
In math terms, and following the notation in the
[paper](https://arxiv.org/abs/1606.07792), the expression for the architecture
without a ``deephead`` component can be formulated as:
<p align="center">
<img width="500" src="mkdocs/sources/docs/figures/architecture_1_math.png">
</p>
Where σ is the sigmoid function, *'W'* are the weight matrices applied to the wide model and to the final
activations of the deep models, *'a'* are these final activations,
φ(x) are the cross product transformations of the original features *'x'*, anExcerpt of 35,777 characters
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:5ab3dfb9e673ac5b, topic:deep-learning, topic:pytorch