sklearn medical es en dataset:fridriik/mental-health-arg-post-quarantine-covid19-dataset arxiv:1910.09700 license:cc-by-nc-4.0 region:us
---
license: cc-by-nc-4.0
datasets:
- fridriik/mental-health-arg-post-quarantine-covid19-dataset
language:
- es
- en
library_name: sklearn
tags:
- medical
metrics:
- perplexity
---

# Mental health of people in Argentina post quarantine COVID-19 Model

## Model Details

### Model Description

This model aims to cluster cases and identify which province or region of Argentina presents higher values of 
suicide risk based on the analyzed variables, in order to subsequently assist the community in creating support programs.

- **Developed by:** Farias, Federico; Arroyo, Guadalupe; Avalos, Manuel
- **Model type:** Clustering
- **License:** Creative Commons Attribution Non Commercial 4.0

## Uses

Research and education.

### Out-of-Scope Use

Government and private entities in the fields of research, medicine, psychology, and education.

## Bias, Risks, and Limitations

This model is intended for research purposes, and it analyzes serious topics related to individuals' mental health. 
It should not be taken as practical advice for real-life situations, except for the possibility that in the future, 
the dataset used for its training could be improved and discussions with its authors could facilitate extended usage.

### Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 

## How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

## Training Details

### Training Data

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on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

https://huggingface.co/datasets/fridriik/mental-health-arg-post-quarantine-covid19-dataset

### Training Procedure 

<!-- This relates heavily to the Technical Specifications. Content here should 
link to that section when it is relevant to the training procedure. -->

#### 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

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

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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]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

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**APA:**

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## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

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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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