{"id":13487080,"url":"https://github.com/pytorch-tabular/pytorch_tabular","last_synced_at":"2026-01-09T22:55:33.245Z","repository":{"id":38456757,"uuid":"321584367","full_name":"manujosephv/pytorch_tabular","owner":"manujosephv","description":"A standard framework for modelling Deep Learning Models for tabular data","archived":false,"fork":false,"pushed_at":"2025-05-12T20:51:17.000Z","size":41536,"stargazers_count":1505,"open_issues_count":17,"forks_count":153,"subscribers_count":18,"default_branch":"main","last_synced_at":"2025-05-12T21:43:51.632Z","etag":null,"topics":["deep-learning","hacktoberfest","machine-learning","pytorch","pytorch-lightning","tabular-data"],"latest_commit_sha":null,"homepage":"https://pytorch-tabular.readthedocs.io/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/manujosephv.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"docs/contributing.md","funding":".github/FUNDING.yml","license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null},"funding":{"github":"manujosephv","patreon":null,"open_collective":null,"ko_fi":null,"tidelift":null,"community_bridge":null,"liberapay":null,"issuehunt":null,"otechie":null,"lfx_crowdfunding":null,"custom":null}},"created_at":"2020-12-15T07:17:03.000Z","updated_at":"2025-05-12T17:39:34.000Z","dependencies_parsed_at":"2023-11-23T01:43:01.042Z","dependency_job_id":"7b948591-1f41-4005-9c3f-550583881ddc","html_url":"https://github.com/manujosephv/pytorch_tabular","commit_stats":{"total_commits":540,"total_committers":23,"mean_commits":23.47826086956522,"dds":0.3648148148148148,"last_synced_commit":"a890ddaa435d452dfbf9a3430a49a94de1898ebb"},"previous_names":[],"tags_count":13,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/manujosephv%2Fpytorch_tabular","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/manujosephv%2Fpytorch_tabular/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/manujosephv%2Fpytorch_tabular/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/manujosephv%2Fpytorch_tabular/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/manujosephv","download_url":"https://codeload.github.com/manujosephv/pytorch_tabular/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":253830792,"owners_count":21970999,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["deep-learning","hacktoberfest","machine-learning","pytorch","pytorch-lightning","tabular-data"],"created_at":"2024-07-31T18:00:55.144Z","updated_at":"2026-01-09T22:55:33.204Z","avatar_url":"https://github.com/manujosephv.png","language":"Python","readme":"![PyTorch Tabular](docs/imgs/pytorch_tabular_logo.png)\n\n[![pypi](https://img.shields.io/pypi/v/pytorch_tabular.svg)](https://pypi.python.org/pypi/pytorch_tabular)\n[![Testing](https://github.com/manujosephv/pytorch_tabular/actions/workflows/testing.yml/badge.svg?event=push)](https://github.com/manujosephv/pytorch_tabular/actions/workflows/testing.yml)\n[![documentation status](https://readthedocs.org/projects/pytorch_tabular/badge/?version=latest)](https://pytorch-tabular.readthedocs.io/en/latest/)\n[![pre-commit.ci status](https://results.pre-commit.ci/badge/github/manujosephv/pytorch_tabular/main.svg)](https://results.pre-commit.ci/latest/github/manujosephv/pytorch_tabular/main)\n[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/manujosephv/pytorch_tabular/blob/main/docs/tutorials/01-Basic_Usage.ipynb)\n\n![PyPI - Downloads](https://img.shields.io/pypi/dm/pytorch_tabular)\n[![DOI](https://zenodo.org/badge/321584367.svg)](https://zenodo.org/badge/latestdoi/321584367)\n[![contributions welcome](https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat-square)](https://github.com/manujosephv/pytorch_tabular/issues)\n\nPyTorch Tabular aims to make Deep Learning with Tabular data easy and accessible to real-world cases and research alike. The core principles behind the design of the library are:\n\n- Low Resistance Usability\n- Easy Customization\n- Scalable and Easier to Deploy\n\nIt has been built on the shoulders of giants like **PyTorch**(obviously), and **PyTorch Lightning**.\n\n## Table of Contents\n\n- [Installation](#installation)\n- [Documentation](#documentation)\n- [Available Models](#available-models)\n- [Usage](#usage)\n- [Blogs](#blogs)\n- [Citation](#citation)\n\n## Installation\n\nAlthough the installation includes PyTorch, the best and recommended way is to first install PyTorch from [here](https://pytorch.org/get-started/locally/), picking up the right CUDA version for your machine.\n\nOnce, you have got Pytorch installed, just use:\n\n```bash\npip install -U “pytorch_tabular[extra]”\n```\n\nto install the complete library with extra dependencies (Weights\u0026Biases \u0026 Plotly).\n\nAnd :\n\n```bash\npip install -U “pytorch_tabular”\n```\n\nfor the bare essentials.\n\nThe sources for pytorch_tabular can be downloaded from the `Github repo`\\_.\n\nYou can either clone the public repository:\n\n```bash\ngit clone git://github.com/manujosephv/pytorch_tabular\n```\n\nOnce you have a copy of the source, you can install it with:\n\n```bash\ncd pytorch_tabular \u0026\u0026 pip install .[extra]\n```\n\n## Documentation\n\nFor complete Documentation with tutorials visit [ReadTheDocs](https://pytorch-tabular.readthedocs.io/en/latest/)\n\n## Available Models\n\n- FeedForward Network with Category Embedding is a simple FF network, but with an Embedding layers for the categorical columns.\n- [Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data](https://arxiv.org/abs/1909.06312) is a model presented in ICLR 2020 and according to the authors have beaten well-tuned Gradient Boosting models on many datasets.\n- [TabNet: Attentive Interpretable Tabular Learning](https://arxiv.org/abs/1908.07442) is another model coming out of Google Research which uses Sparse Attention in multiple steps of decision making to model the output.\n- [Mixture Density Networks](https://publications.aston.ac.uk/id/eprint/373/1/NCRG_94_004.pdf) is a regression model which uses gaussian components to approximate the target function and  provide a probabilistic prediction out of the box.\n- [AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks](https://arxiv.org/abs/1810.11921) is a model which tries to learn interactions between the features in an automated way and create a better representation and then use this representation in downstream task\n- [TabTransformer](https://arxiv.org/abs/2012.06678) is an adaptation of the Transformer model for Tabular Data which creates contextual representations for categorical features.\n- FT Transformer from [Revisiting Deep Learning Models for Tabular Data](https://arxiv.org/abs/2106.11959)\n- [Gated Additive Tree Ensemble](https://arxiv.org/abs/2207.08548v3) is a novel high-performance, parameter and computationally efficient deep learning architecture for tabular data. GATE uses a gating mechanism, inspired from GRU, as a feature representation learning unit with an in-built feature selection mechanism. We combine it with an ensemble of differentiable, non-linear decision trees, re-weighted with simple self-attention to predict our desired output.\n- [Gated Adaptive Network for Deep Automated Learning of Features (GANDALF)](https://arxiv.org/abs/2207.08548) is pared-down version of GATE which is more efficient and performing than GATE. GANDALF makes GFLUs the main learning unit, also introducing some speed-ups in the process. With very minimal hyperparameters to tune, this becomes an easy to use and tune model.\n- [DANETs: Deep Abstract Networks for Tabular Data Classification and Regression](https://arxiv.org/pdf/2112.02962v4.pdf) is a novel and flexible neural component for tabular data, called Abstract Layer (AbstLay), which learns to explicitly group correlative input features and generate higher-level features for semantics abstraction.  A special basic block is built using AbstLays, and we construct a family of Deep Abstract Networks (DANets) for tabular data classification and regression by stacking such blocks.\n\n**Semi-Supervised Learning**\n\n- [Denoising AutoEncoder](https://www.kaggle.com/code/springmanndaniel/1st-place-turn-your-data-into-daeta) is an autoencoder which learns robust feature representation, to compensate any noise in the dataset.\n\n## Implement Custom Models\nTo implement new models, see the [How to implement new models tutorial](https://github.com/manujosephv/pytorch_tabular/blob/main/docs/tutorials/04-Implementing%20New%20Architectures.ipynb). It covers basic as well as advanced architectures.\n\n## Usage\n\n```python\nfrom pytorch_tabular import TabularModel\nfrom pytorch_tabular.models import CategoryEmbeddingModelConfig\nfrom pytorch_tabular.config import (\n    DataConfig,\n    OptimizerConfig,\n    TrainerConfig,\n    ExperimentConfig,\n)\n\ndata_config = DataConfig(\n    target=[\n        \"target\"\n    ],  # target should always be a list.\n    continuous_cols=num_col_names,\n    categorical_cols=cat_col_names,\n)\ntrainer_config = TrainerConfig(\n    auto_lr_find=True,  # Runs the LRFinder to automatically derive a learning rate\n    batch_size=1024,\n    max_epochs=100,\n)\noptimizer_config = OptimizerConfig()\n\nmodel_config = CategoryEmbeddingModelConfig(\n    task=\"classification\",\n    layers=\"1024-512-512\",  # Number of nodes in each layer\n    activation=\"LeakyReLU\",  # Activation between each layers\n    learning_rate=1e-3,\n)\n\ntabular_model = TabularModel(\n    data_config=data_config,\n    model_config=model_config,\n    optimizer_config=optimizer_config,\n    trainer_config=trainer_config,\n)\ntabular_model.fit(train=train, validation=val)\nresult = tabular_model.evaluate(test)\npred_df = tabular_model.predict(test)\ntabular_model.save_model(\"examples/basic\")\nloaded_model = TabularModel.load_model(\"examples/basic\")\n```\n\n## Blogs\n\n- [PyTorch Tabular – A Framework for Deep Learning for Tabular Data](https://deep-and-shallow.com/2021/01/27/pytorch-tabular-a-framework-for-deep-learning-for-tabular-data/)\n- [Neural Oblivious Decision Ensembles(NODE) – A State-of-the-Art Deep Learning Algorithm for Tabular Data](https://deep-and-shallow.com/2021/02/25/neural-oblivious-decision-ensemblesnode-a-state-of-the-art-deep-learning-algorithm-for-tabular-data/)\n- [Mixture Density Networks: Probabilistic Regression for Uncertainty Estimation](https://deep-and-shallow.com/2021/03/20/mixture-density-networks-probabilistic-regression-for-uncertainty-estimation/)\n\n## Future Roadmap(Contributions are Welcome)\n\n1. Integrate Optuna Hyperparameter Tuning\n1. Migrate Datamodule to Polars or NVTabular for faster data loading and to handle larger than RAM datasets.\n1. Add GaussRank as Feature Transformation\n1. Have a scikit-learn compatible API\n1. Enable support for multi-label classification\n1. Keep adding more architectures\n\n## Contributors\n\n\u003c!-- readme: contributors -start --\u003e\n\u003ctable\u003e\n\t\u003ctbody\u003e\n\t\t\u003ctr\u003e\n            \u003ctd align=\"center\"\u003e\n                \u003ca href=\"https://github.com/manujosephv\"\u003e\n                    \u003cimg src=\"https://avatars.githubusercontent.com/u/10508493?v=4\" width=\"100;\" alt=\"manujosephv\"/\u003e\n                    \u003cbr /\u003e\n                    \u003csub\u003e\u003cb\u003eManu Joseph\u003c/b\u003e\u003c/sub\u003e\n                \u003c/a\u003e\n            \u003c/td\u003e\n            \u003ctd align=\"center\"\u003e\n                \u003ca href=\"https://github.com/Borda\"\u003e\n                    \u003cimg src=\"https://avatars.githubusercontent.com/u/6035284?v=4\" width=\"100;\" alt=\"Borda\"/\u003e\n                    \u003cbr /\u003e\n                    \u003csub\u003e\u003cb\u003eJirka Borovec\u003c/b\u003e\u003c/sub\u003e\n                \u003c/a\u003e\n            \u003c/td\u003e\n            \u003ctd align=\"center\"\u003e\n                \u003ca href=\"https://github.com/wsad1\"\u003e\n                    \u003cimg src=\"https://avatars.githubusercontent.com/u/13963626?v=4\" width=\"100;\" alt=\"wsad1\"/\u003e\n                    \u003cbr /\u003e\n                    \u003csub\u003e\u003cb\u003eJinu Sunil\u003c/b\u003e\u003c/sub\u003e\n                \u003c/a\u003e\n            \u003c/td\u003e\n            \u003ctd align=\"center\"\u003e\n                \u003ca href=\"https://github.com/ProgramadorArtificial\"\u003e\n                    \u003cimg src=\"https://avatars.githubusercontent.com/u/130674366?v=4\" width=\"100;\" alt=\"ProgramadorArtificial\"/\u003e\n                    \u003cbr /\u003e\n                    \u003csub\u003e\u003cb\u003eProgramador Artificial\u003c/b\u003e\u003c/sub\u003e\n                \u003c/a\u003e\n            \u003c/td\u003e\n            \u003ctd align=\"center\"\u003e\n                \u003ca href=\"https://github.com/sorenmacbeth\"\u003e\n                    \u003cimg src=\"https://avatars.githubusercontent.com/u/130043?v=4\" width=\"100;\" alt=\"sorenmacbeth\"/\u003e\n                    \u003cbr /\u003e\n                    \u003csub\u003e\u003cb\u003eSoren Macbeth\u003c/b\u003e\u003c/sub\u003e\n                \u003c/a\u003e\n            \u003c/td\u003e\n            \u003ctd align=\"center\"\u003e\n                \u003ca href=\"https://github.com/ArozHada\"\u003e\n                    \u003cimg src=\"https://avatars.githubusercontent.com/u/19288227?v=4\" width=\"100;\" alt=\"ArozHada\"/\u003e\n                    \u003cbr /\u003e\n                    \u003csub\u003e\u003cb\u003eAroj Hada\u003c/b\u003e\u003c/sub\u003e\n                \u003c/a\u003e\n            \u003c/td\u003e\n\t\t\u003c/tr\u003e\n\t\t\u003ctr\u003e\n            \u003ctd align=\"center\"\u003e\n                \u003ca href=\"https://github.com/fonnesbeck\"\u003e\n                    \u003cimg src=\"https://avatars.githubusercontent.com/u/81476?v=4\" width=\"100;\" alt=\"fonnesbeck\"/\u003e\n                    \u003cbr /\u003e\n                    \u003csub\u003e\u003cb\u003eChris Fonnesbeck\u003c/b\u003e\u003c/sub\u003e\n                \u003c/a\u003e\n            \u003c/td\u003e\n            \u003ctd align=\"center\"\u003e\n                \u003ca href=\"https://github.com/snehilchatterjee\"\u003e\n                    \u003cimg src=\"https://avatars.githubusercontent.com/u/127598707?v=4\" width=\"100;\" alt=\"snehilchatterjee\"/\u003e\n                    \u003cbr /\u003e\n                    \u003csub\u003e\u003cb\u003eSnehil Chatterjee\u003c/b\u003e\u003c/sub\u003e\n                \u003c/a\u003e\n            \u003c/td\u003e\n            \u003ctd align=\"center\"\u003e\n                \u003ca href=\"https://github.com/jxtrbtk\"\u003e\n                    \u003cimg src=\"https://avatars.githubusercontent.com/u/40494970?v=4\" width=\"100;\" alt=\"jxtrbtk\"/\u003e\n                    \u003cbr /\u003e\n                    \u003csub\u003e\u003cb\u003eNull\u003c/b\u003e\u003c/sub\u003e\n                \u003c/a\u003e\n            \u003c/td\u003e\n            \u003ctd align=\"center\"\u003e\n                \u003ca href=\"https://github.com/abhisharsinha\"\u003e\n                    \u003cimg src=\"https://avatars.githubusercontent.com/u/24841841?v=4\" width=\"100;\" alt=\"abhisharsinha\"/\u003e\n                    \u003cbr /\u003e\n                    \u003csub\u003e\u003cb\u003eAbhishar Sinha\u003c/b\u003e\u003c/sub\u003e\n                \u003c/a\u003e\n            \u003c/td\u003e\n            \u003ctd align=\"center\"\u003e\n                \u003ca href=\"https://github.com/ndrsfel\"\u003e\n                    \u003cimg src=\"https://avatars.githubusercontent.com/u/21068727?v=4\" width=\"100;\" alt=\"ndrsfel\"/\u003e\n                    \u003cbr /\u003e\n                    \u003csub\u003e\u003cb\u003eAndreas\u003c/b\u003e\u003c/sub\u003e\n                \u003c/a\u003e\n            \u003c/td\u003e\n            \u003ctd align=\"center\"\u003e\n                \u003ca href=\"https://github.com/charitarthchugh\"\u003e\n                    \u003cimg src=\"https://avatars.githubusercontent.com/u/37895518?v=4\" width=\"100;\" alt=\"charitarthchugh\"/\u003e\n                    \u003cbr /\u003e\n                    \u003csub\u003e\u003cb\u003eCharitarth Chugh\u003c/b\u003e\u003c/sub\u003e\n                \u003c/a\u003e\n            \u003c/td\u003e\n\t\t\u003c/tr\u003e\n\t\t\u003ctr\u003e\n            \u003ctd align=\"center\"\u003e\n                \u003ca href=\"https://github.com/EeyoreLee\"\u003e\n                    \u003cimg src=\"https://avatars.githubusercontent.com/u/49790022?v=4\" width=\"100;\" alt=\"EeyoreLee\"/\u003e\n                    \u003cbr /\u003e\n                    \u003csub\u003e\u003cb\u003eEarlee\u003c/b\u003e\u003c/sub\u003e\n                \u003c/a\u003e\n            \u003c/td\u003e\n            \u003ctd align=\"center\"\u003e\n                \u003ca href=\"https://github.com/JulianRein\"\u003e\n                    \u003cimg src=\"https://avatars.githubusercontent.com/u/35046938?v=4\" width=\"100;\" alt=\"JulianRein\"/\u003e\n                    \u003cbr /\u003e\n                    \u003csub\u003e\u003cb\u003eNull\u003c/b\u003e\u003c/sub\u003e\n                \u003c/a\u003e\n            \u003c/td\u003e\n            \u003ctd align=\"center\"\u003e\n                \u003ca href=\"https://github.com/krshrimali\"\u003e\n                    \u003cimg src=\"https://avatars.githubusercontent.com/u/19997320?v=4\" width=\"100;\" alt=\"krshrimali\"/\u003e\n                    \u003cbr /\u003e\n                    \u003csub\u003e\u003cb\u003eKushashwa Ravi Shrimali\u003c/b\u003e\u003c/sub\u003e\n                \u003c/a\u003e\n            \u003c/td\u003e\n            \u003ctd align=\"center\"\u003e\n                \u003ca href=\"https://github.com/Actis92\"\u003e\n                    \u003cimg src=\"https://avatars.githubusercontent.com/u/46601193?v=4\" width=\"100;\" alt=\"Actis92\"/\u003e\n                    \u003cbr /\u003e\n                    \u003csub\u003e\u003cb\u003eLuca Actis Grosso\u003c/b\u003e\u003c/sub\u003e\n                \u003c/a\u003e\n            \u003c/td\u003e\n            \u003ctd align=\"center\"\u003e\n                \u003ca href=\"https://github.com/sgbaird\"\u003e\n                    \u003cimg src=\"https://avatars.githubusercontent.com/u/45469701?v=4\" width=\"100;\" alt=\"sgbaird\"/\u003e\n                    \u003cbr /\u003e\n                    \u003csub\u003e\u003cb\u003eSterling G. Baird\u003c/b\u003e\u003c/sub\u003e\n                \u003c/a\u003e\n            \u003c/td\u003e\n            \u003ctd align=\"center\"\u003e\n                \u003ca href=\"https://github.com/furyhawk\"\u003e\n                    \u003cimg src=\"https://avatars.githubusercontent.com/u/831682?v=4\" width=\"100;\" alt=\"furyhawk\"/\u003e\n                    \u003cbr /\u003e\n                    \u003csub\u003e\u003cb\u003eTeck Meng\u003c/b\u003e\u003c/sub\u003e\n                \u003c/a\u003e\n            \u003c/td\u003e\n\t\t\u003c/tr\u003e\n\t\t\u003ctr\u003e\n            \u003ctd align=\"center\"\u003e\n                \u003ca href=\"https://github.com/yinyunie\"\u003e\n                    \u003cimg src=\"https://avatars.githubusercontent.com/u/25686434?v=4\" width=\"100;\" alt=\"yinyunie\"/\u003e\n                    \u003cbr /\u003e\n                    \u003csub\u003e\u003cb\u003eYinyu Nie\u003c/b\u003e\u003c/sub\u003e\n                \u003c/a\u003e\n            \u003c/td\u003e\n            \u003ctd align=\"center\"\u003e\n                \u003ca href=\"https://github.com/YonyBresler\"\u003e\n                    \u003cimg src=\"https://avatars.githubusercontent.com/u/24940683?v=4\" width=\"100;\" alt=\"YonyBresler\"/\u003e\n                    \u003cbr /\u003e\n                    \u003csub\u003e\u003cb\u003eYonyBresler\u003c/b\u003e\u003c/sub\u003e\n                \u003c/a\u003e\n            \u003c/td\u003e\n            \u003ctd align=\"center\"\u003e\n                \u003ca href=\"https://github.com/HernandoR\"\u003e\n                    \u003cimg src=\"https://avatars.githubusercontent.com/u/45709656?v=4\" width=\"100;\" alt=\"HernandoR\"/\u003e\n                    \u003cbr /\u003e\n                    \u003csub\u003e\u003cb\u003eLiu Zhen\u003c/b\u003e\u003c/sub\u003e\n                \u003c/a\u003e\n            \u003c/td\u003e\n            \u003ctd align=\"center\"\u003e\n                \u003ca href=\"https://github.com/enifeder\"\u003e\n                    \u003cimg src=\"https://avatars.githubusercontent.com/u/65483484?v=4\" width=\"100;\" alt=\"enifeder\"/\u003e\n                    \u003cbr /\u003e\n                    \u003csub\u003e\u003cb\u003eenifeder\u003c/b\u003e\u003c/sub\u003e\n                \u003c/a\u003e\n            \u003c/td\u003e\n            \u003ctd align=\"center\"\u003e\n                \u003ca href=\"https://github.com/taimo3810\"\u003e\n                    \u003cimg src=\"https://avatars.githubusercontent.com/u/132860814?v=4\" width=\"100;\" alt=\"taimo3810\"/\u003e\n                    \u003cbr /\u003e\n                    \u003csub\u003e\u003cb\u003etaimo\u003c/b\u003e\u003c/sub\u003e\n                \u003c/a\u003e\n            \u003c/td\u003e\n\t\t\u003c/tr\u003e\n\t\u003ctbody\u003e\n\u003c/table\u003e\n\u003c!-- readme: contributors -end --\u003e\n\n## Citation\n\nIf you use PyTorch Tabular for a scientific publication, we would appreciate citations to the published software and the following paper:\n\n- [arxiv Paper](https://arxiv.org/abs/2104.13638)\n\n```\n@misc{joseph2021pytorch,\n      title={PyTorch Tabular: A Framework for Deep Learning with Tabular Data},\n      author={Manu Joseph},\n      year={2021},\n      eprint={2104.13638},\n      archivePrefix={arXiv},\n      primaryClass={cs.LG}\n}\n```\n\n- Zenodo Software Citation\n\n```\n@software{manu_joseph_2023_7554473,\n  author       = {Manu Joseph and\n                  Jinu Sunil and\n                  Jiri Borovec and\n                  Chris Fonnesbeck and\n                  jxtrbtk and\n                  Andreas and\n                  JulianRein and\n                  Kushashwa Ravi Shrimali and\n                  Luca Actis Grosso and\n                  Sterling G. Baird and\n                  Yinyu Nie},\n  title        = {manujosephv/pytorch\\_tabular: v1.0.1},\n  month        = jan,\n  year         = 2023,\n  publisher    = {Zenodo},\n  version      = {v1.0.1},\n  doi          = {10.5281/zenodo.7554473},\n  url          = {https://doi.org/10.5281/zenodo.7554473}\n}\n```\n","funding_links":["https://github.com/sponsors/manujosephv"],"categories":["The Data Science Toolbox","Python","其他_机器学习与深度学习","Tools \u0026 Libraries"],"sub_categories":["Deep Learning Packages","Hybrid Learning"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpytorch-tabular%2Fpytorch_tabular","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpytorch-tabular%2Fpytorch_tabular","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpytorch-tabular%2Fpytorch_tabular/lists"}