arxiv:1910.09700 region:us
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# Model Card for Model ID

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This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).

## Model Details

### Model Description

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- **Developed by:** Hilary, Evan, Matthis, Valère, Doline, Christian, Sorel
- **Funded by [optional]:**
- **Shared by [optional]:** 
- **Model type:** Linear
- **Language(s) (NLP):** Python
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]

### Model Sources [optional]

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- **Repository:** Model factory
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- **Demo [optional]:** [More Information Needed]

## Uses

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### Direct Use

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

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## Training Details
We have a dataset containing information on energy consumption in France, in particular fossil fuels and renewable energies per year, as well as information on the number of inhabitants (rural and urban areas), GDP per capita and life expectancy.

### Training Data
We used the variables Temps, population and all fossil. 
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### Training Procedure 

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The model was trained with a linear model called OLS (Ordinary Least Squares), from the statsmodels package.

#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

We have an R-squared score of 0.597, i.e. the variables we have selected explain almost 60% of the target variable. 
The variables Temps and Population have p-values of 0.7 and 0.3, which are greater than 0.1, i.e. they do not have a great influence on renewable energy consumption. On the other hand, the All fossil variable has a p-value of 0.03, which has a strong influence on renewable energy consumption.

#### Summary



## Model Examination [optional]

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## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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