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<details><summary>Pre-requisites only for Macbook Pro with M1 Pro chip users</summary>

<p>

- To install TensorFlow you can follow this [tutorial](https://betterdatascience.com/install-tensorflow-2-7-on-macbook-pro-m1-pro/).
- To install TensorFlow Addons you can follow these [steps](https://github.com/tensorflow/addons/pull/2504). For further reading see this [issue](https://github.com/tensorflow/addons/issues/2503). Note that this temporary solution works only for macOS versions >= 12.0. Note that this step may have changed already, see https://github.com/dianna-ai/dianna/issues/245.
- Before installing DIANNA, comment `tensorflow` requirement in `setup.cfg` file (tensorflow package for M1 is called `tensorflow-macos`).
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### Text and image usage
<details><summary>Examples</summary>

<p>

Lets illustrate the template above with *textual* data. The data item of interest is a sentence being (a part of) a movie review and the model has been trained to classify reviews into positive and negative sentiment classes.
We are intersted which words are contributing positively (red) and which - negatively (blue) towards the model's desicion to classify the review as positive and we would like to use the *LIME* explainer:

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</details>

<details><summary>Text</summary>

<p>

| Dataset | Description | Examples | Generation |
| :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :---------------------------------------------------------------------------- | :--------------------------------------------------------------- | :------------------------------------------------------------------ |
| [Stanford sentiment treebank](https://nlp.stanford.edu/sentiment/index.html) <img width="20" alt="nlp-logo_half_size" src="https://user-images.githubusercontent.com/3244249/152355020-908c04f3-aa99-489d-b87a-7e6b1f586118.png"> | Dataset for predicting the sentiment, positive or negative, of movie reviews. | _This movie was actually neither that funny, nor super witty._ | [Sentiment treebank](https://nlp.stanford.edu/sentiment/treebank.html) |
| [EU-law statements](https://zenodo.org/records/8200000) <img width="25" alt="nlp-logo_half_size" src="https://avatars.githubusercontent.com/u/133206807?s=48&v=4"> | Reproducibility data for a quantitative study on EU legislation. | _A Member State wishing to grant exemptions referred to in paragraph 6 shall notify the Council in writing_ | [EU legislation strictness analysis](https://github.com/nature-of-eu-rules/eu-legislation-strictness-analysis) |

</details>

<details><summary>Time series</summary>

<p>

| Dataset | Description | Examples | Generation |
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</details>

<details><summary>Tabular</summary>

<p>

| Dataset | Description | Examples | Generation |
| :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------- | :------------------------------------------------------------------------ |
| [Pengiun dataset](https://www.kaggle.com/code/parulpandey/penguin-dataset-the-new-iris) <img width="75" alt="Penguins Logo" src="https://github.com/dianna-ai/dianna/assets/3244249/c7716ad3-f992-4557-80d9-1d8178c7ed57"> | Palmer Archipelago (Antarctica) penguin dataset is a great intro dataset for data exploration & visualization similar to the famous Iris dataset. | <img width="500" alt="example image" src="https://github.com/allisonhorst/palmerpenguins/blob/main/man/figures/README-mass-flipper-1.png"> | [data source](https://github.com/allisonhorst/palmerpenguins) |
| [Weather dataset](https://zenodo.org/record/7525955) <img width="25" alt="Weather Logo" src="https://github.com/dianna-ai/dianna/assets/3244249/3ff3d639-ed2f-4a38-b7ac-957c984bce9f"> | The light version of the weather prediciton dataset, which contains daily observations (89 features) for 11 European locations through the years 2000 to 2010. | <img width="500" alt="example image" src="https://github.com/dianna-ai/dianna/assets/3244249/b0a505ac-8a6c-4e1c-b6ad-35e31e52f46d"> | [data source](https://github.com/florian-huber/weather_prediction_dataset) |
| [Land atmosphere dataset](https://zenodo.org/records/12623257) <img width="25" alt="Atmosphere Logo" src="https://github.com/user-attachments/assets/bee353dd-c19a-4aec-a778-4ca3574765f0"> | It contains land-atmosphere variables and latent heat flux (LEtot) simulated by STEMMUS-SCOPE (soil-plant model), version 1.5.0, over 19 Fluxnet sites and for the year 2014 with hourly intervals. | <img width="500" alt="example image" src="https://github.com/user-attachments/assets/a6e10b08-08d8-4e57-887a-cd4fca9f2ff0"> | [data source](https://zenodo.org/records/12623257) |

</details>


## Models

**We work with ONNX!** ONNX is a great unified neural network standard which can be used to boost reproducible science. Using ONNX for your model also gives you a boost in performance! In case your models are still in another popular DNN (deep neural network) format, here are some simple recipes to convert them:
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| [Binary MNIST model](https://zenodo.org/record/5907177) | [Binary MNIST model generation](https://github.com/dianna-ai/dianna-exploration/blob/main/example_data/model_generation/MNIST/generate_model_binary.ipynb) |
| [Simple Geometric model](https://zenodo.org/deposit/5907059) | [Simple geometric shapes model generation](https://github.com/dianna-ai/dianna-exploration/blob/main/example_data/model_generation/geometric_shapes/generate_model.ipynb) |
| [Simple Scientific model](https://zenodo.org/record/5907196) | [LeafSnap30 model generation](https://github.com/dianna-ai/dianna-exploration/blob/main/example_data/model_generation/LeafSnap/generate_model.ipynb) |

</details>

<details><summary>Text</summary>
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