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README.md

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<p align="center">
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<img src="https://raw.githubusercontent.com/pyc-team/pytorch_concepts/refs/heads/factors/doc/_static/img/pyc_logo.png" alt="PyC Logo" width="40%">
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<img src="https://raw.githubusercontent.com/pyc-team/pytorch_concepts/refs/heads/master/doc/_static/img/pyc_logo.png" alt="PyC Logo" width="40%">
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<a href="https://pytorch-concepts.readthedocs.io/en/latest/guides/using.html">💻 User guide</a>
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</p>
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<img src="https://raw.githubusercontent.com/pyc-team/pytorch_concepts/refs/heads/factors/doc/_static/img/logos/pyc.svg" width="20px"> PyC is a library built upon <img src="https://raw.githubusercontent.com/pyc-team/pytorch_concepts/refs/heads/factors/doc/_static/img/logos/pytorch.svg" width="20px" align="center"> PyTorch and <img src="https://raw.githubusercontent.com/pyc-team/pytorch_concepts/refs/heads/factors/doc/_static/img/logos/lightning.svg" width="20px" align="center"> Pytorch Lightning to easily implement **interpretable and causally transparent deep learning models**.
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<img src="https://raw.githubusercontent.com/pyc-team/pytorch_concepts/refs/heads/master/doc/_static/img/logos/pyc.svg" width="20px"> PyC is a library built upon <img src="https://raw.githubusercontent.com/pyc-team/pytorch_concepts/refs/heads/master/doc/_static/img/logos/pytorch.svg" width="20px" align="center"> PyTorch and <img src="https://raw.githubusercontent.com/pyc-team/pytorch_concepts/refs/heads/master/doc/_static/img/logos/lightning.svg" width="20px" align="center"> Pytorch Lightning to easily implement **interpretable and causally transparent deep learning models**.
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The library provides primitives for layers (encoders, predictors, special layers), probabilistic models, and APIs for running experiments at scale.
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# Quick Start
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You can install <img src="https://raw.githubusercontent.com/pyc-team/pytorch_concepts/refs/heads/factors/doc/_static/img/logos/pyc.svg" width="20px"> PyC with core dependencies from [PyPI](https://pypi.org/project/pytorch-concepts/):
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You can install <img src="https://raw.githubusercontent.com/pyc-team/pytorch_concepts/refs/heads/master/doc/_static/img/logos/pyc.svg" width="20px"> PyC with core dependencies from [PyPI](https://pypi.org/project/pytorch-concepts/):
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```bash
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pip install pytorch-concepts
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import torch_concepts as pyc
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```
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Follow our [user guide](https://pytorch-concepts.readthedocs.io/en/latest/guides/using.html) to get started with building interpretable models using <img src="https://raw.githubusercontent.com/pyc-team/pytorch_concepts/refs/heads/factors/doc/_static/img/logos/pyc.svg" width="20px"> PyC!
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Follow our [user guide](https://pytorch-concepts.readthedocs.io/en/latest/guides/using.html) to get started with building interpretable models using <img src="https://raw.githubusercontent.com/pyc-team/pytorch_concepts/refs/heads/master/doc/_static/img/logos/pyc.svg" width="20px"> PyC!
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# <img src="https://raw.githubusercontent.com/pyc-team/pytorch_concepts/refs/heads/factors/doc/_static/img/logos/pyc.svg" width="20px"> PyC Software Stack
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# <img src="https://raw.githubusercontent.com/pyc-team/pytorch_concepts/refs/heads/master/doc/_static/img/logos/pyc.svg" width="20px"> PyC Software Stack
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The library is organized to be modular and accessible at different levels of abstraction:
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- <img src="https://raw.githubusercontent.com/pyc-team/pytorch_concepts/refs/heads/factors/doc/_static/img/logos/conceptarium.svg" width="20px" align="center"> **Conceptarium (No-code API). Use case: applications and benchmarking.** These APIs allow to easily run large-scale highly parallelized and standardized experiments by interfacing with configuration files. Built on top of <img src="https://raw.githubusercontent.com/pyc-team/pytorch_concepts/refs/heads/factors/doc/_static/img/logos/hydra-head.svg" width="20px" align="center"> Hydra and <img src="https://raw.githubusercontent.com/pyc-team/pytorch_concepts/refs/heads/factors/doc/_static/img/logos/wandb.svg" width="20px" align="center"> WandB.
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- **High-level APIs. Use case: use out-of-the-box state-of-the-art models.** These APIs allow to instantiate use implemented models with 1 line of code. This interface is built in <img src="https://raw.githubusercontent.com/pyc-team/pytorch_concepts/refs/heads/factors/doc/_static/img/logos/lightning.svg" width="20px" align="center"> Pytorch Lightning to easily standardize training and evaluation.
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- <img src="https://raw.githubusercontent.com/pyc-team/pytorch_concepts/refs/heads/master/doc/_static/img/logos/conceptarium.svg" width="20px" align="center"> **Conceptarium (No-code API). Use case: applications and benchmarking.** These APIs allow to easily run large-scale highly parallelized and standardized experiments by interfacing with configuration files. Built on top of <img src="https://raw.githubusercontent.com/pyc-team/pytorch_concepts/refs/heads/master/doc/_static/img/logos/hydra-head.svg" width="20px" align="center"> Hydra and <img src="https://raw.githubusercontent.com/pyc-team/pytorch_concepts/refs/heads/master/doc/_static/img/logos/wandb.svg" width="20px" align="center"> WandB.
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- **High-level APIs. Use case: use out-of-the-box state-of-the-art models.** These APIs allow to instantiate use implemented models with 1 line of code. This interface is built in <img src="https://raw.githubusercontent.com/pyc-team/pytorch_concepts/refs/heads/master/doc/_static/img/logos/lightning.svg" width="20px" align="center"> Pytorch Lightning to easily standardize training and evaluation.
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- **Mid-level APIs. Use case: build custom interpretable and causally transparent probabilistic graphical models.** These APIs allow to build new interpretable probabilistic models and run efficient tensorial probabilistic inference.
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- **Low-level APIs. Use case: assemble custom interpretable architectures.** These APIs allow to build architectures from basic interpretable layers in a plain <img src="https://raw.githubusercontent.com/pyc-team/pytorch_concepts/refs/heads/factors/doc/_static/img/logos/pytorch.svg" width="20px" align="center"> PyTorch-like interface. These APIs also include metrics, losses, and datasets.
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- **Low-level APIs. Use case: assemble custom interpretable architectures.** These APIs allow to build architectures from basic interpretable layers in a plain <img src="https://raw.githubusercontent.com/pyc-team/pytorch_concepts/refs/heads/master/doc/_static/img/logos/pytorch.svg" width="20px" align="center"> PyTorch-like interface. These APIs also include metrics, losses, and datasets.
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<p align="center">
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<img src="https://raw.githubusercontent.com/pyc-team/pytorch_concepts/refs/heads/factors/doc/_static/img/pyc_software_stack.png" alt="PyC Software Stack" width="90%">
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<img src="https://raw.githubusercontent.com/pyc-team/pytorch_concepts/refs/heads/master/doc/_static/img/pyc_software_stack.png" alt="PyC Software Stack" width="90%">
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---
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This project is supported by the following organizations:
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<img src="https://raw.githubusercontent.com/pyc-team/pytorch_concepts/refs/heads/factors/doc/_static/img/funding/fwo_kleur.png" alt="FWO - Research Foundation Flanders" height="60" style="margin: 20px;">
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<img src="https://raw.githubusercontent.com/pyc-team/pytorch_concepts/refs/heads/master/doc/_static/img/funding/fwo_kleur.png" alt="FWO - Research Foundation Flanders" height="60" style="margin: 20px;">
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&nbsp;&nbsp;&nbsp;&nbsp;
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<img src="https://raw.githubusercontent.com/pyc-team/pytorch_concepts/refs/heads/factors/doc/_static/img/funding/hasler.png" alt="Hasler Foundation" height="60" style="margin: 20px;">
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<img src="https://raw.githubusercontent.com/pyc-team/pytorch_concepts/refs/heads/master/doc/_static/img/funding/hasler.png" alt="Hasler Foundation" height="60" style="margin: 20px;">
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&nbsp;&nbsp;&nbsp;&nbsp;
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<img src="https://raw.githubusercontent.com/pyc-team/pytorch_concepts/refs/heads/factors/doc/_static/img/funding/snsf.png" alt="SNSF - Swiss National Science Foundation" height="60" style="margin: 20px;">
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<img src="https://raw.githubusercontent.com/pyc-team/pytorch_concepts/refs/heads/master/doc/_static/img/funding/snsf.png" alt="SNSF - Swiss National Science Foundation" height="60" style="margin: 20px;">
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doc/guides/using.rst

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doc/guides/using_high_level.rst

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Out-of-the-box Models
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doc/guides/using_low_level.rst

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The Low-Level API provides building blocks to create concept-based models using
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