---
library_name: transformers
widget:
- text: Mexico_City _GEO Vamos a comer <mask>.
- text: Monterrey _GEO Vamos a comer <mask>.
- text: Tijuana _GEO Vamos a comer <mask>.
license: mit
language:
- es
---

# Model Card for Model ID

This is a Language Model trained with regional tweets from Mexico using MLM.



## Model Details

### Model Description

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The model use the Roberta architecture. 
It was trained from random weights using like 110 million tweets from Mexico with an aditional label indicating the State from procedence.

The tweets had the following structure:

*STATE* _GEO text_from_tweet

The users and url's from the text were replaced by the tokens _USR and _URL respectively.


- **Developed by:** INFOTEC
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** Roberta
- **Language(s) (NLP):** Spanish
- **License:** MIT


### Model Sources [optional]

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## Uses

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The model is intended to be used to extract regional information from Mexico.

### Direct Use

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The masked token can be used to predict the region of the text. Additionaly, the mask prediction can be used for Information Retrival.

### Downstream Use [optional]

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The model can be fine-tuned to be used in tasks like Sentiment Analisys, Classification, 

### 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

### Training Data

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### Training Procedure 

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#### Preprocessing [optional]

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#### Training Hyperparameters

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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

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#### Summary



## Model Examination [optional]

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## Environmental Impact

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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).

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## Technical Specifications [optional]

### Model Architecture and Objective

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## Citation [optional]

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**APA:**

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## Glossary [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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