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# Model Card for Model Geo-BERT-multilingual

<!-- Provide a quick summary of what the model is/does. -->

This model predicts the geolocation of short texts (less than 500 words) in a form of two-dimensional distributions also referenced as the Gaussian Mixture Model (GMM).

## Model Details

Number of predicted points: 5
Custom transformers pipeline and result visualization: https://github.com/K4TEL/geo-twitter/tree/predict

### Model Description

<!-- Provide a longer summary of what this model is. -->

This project was aimed to solve the tweet/user geolocation prediction task and provide a flexible methodology for the geotagging of textual big data. 
The suggested approach implements BERT-based neural networks for NLP to estimate the location in a form of two-dimensional GMMs (longitude, latitude, weight, covariance). 
The base model has been finetuned on a Twitter dataset containing text content and metadata context of the tweets. 

- **Developed by:** Kateryna Lutsai
- **Model type:** regression
- **Language(s) (NLP):** multilingual
- **Finetuned from model:** bert-base-multilingual-cased

### Model Sources

<!-- Provide the basic links for the model. -->

- **Repository:** https://github.com/K4TEL/geo-twitter
- **Paper:** https://arxiv.org/pdf/2303.07865.pdf
- **Demo:** https://github.com/K4TEL/geo-twitter/blob/predict/prediction.ipynb

## Uses

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Geo-tagging of Big data

### Direct Use

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Per-tweet geolocation prediction 

### Out-of-Scope Use

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Per-tweet geolocation prediction without "user" metadata is expected to show lower accuracy of predictions. 

## Bias, Risks, and Limitations

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Risk for unethical use on the basis of data that is not publicly available.

The limitation of text length is dictated by the BERT-based model's capacity of 500 tokens (words).


### How to Get Started with the Model

Use the code below to get started with the model:

https://github.com/K4TEL/geo-twitter/tree/predict

A short startup guide is given in the repository branch description. 

## Training Details

### Training Data

<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

The Twitter dataset contained tweets with their text content, metadata ("user" and "place") context, and geolocation coordinates. 

### Training Procedure 

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Information about the model training on the user-defined data could be found in the GitHub repository: https://github.com/K4TEL/geo-twitter

#### Training Hyperparameters

- **Learning rate start:** 1e-5
- **Learning rate end:** 1e-6
- **Learning rate scheduler:** cosine
- **Number of epochs:** 3
- **Batch size:** 10
- **Optimizer:** Adam
- **Intra-feature loss:** mean
- **Inter-feature loss:** mean
- **Neg log-likelihood domain:** positive
- **Features:** NON-GEO + GEO-ONLY

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->
All performance metrics and results are demonstrated in the Results section of the article pre-print: https://arxiv.org/pdf/2303.07865.pdf

### Testing Data, Factors & Metrics

#### Testing Data

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Worldwide dataset of tweets with TEXT-ONLY and NON-GEO features

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

Spatial metrics: mean and median Simple Accuracy Error (SAE), Acc@161
Probabilistic metrics: mean and median Cumulative Accuracy Error (CAE), mean and median Prediction Area Region (PRA) for 95% density area, Coverage of PRA

### Results

**Tweet geolocation prediction task**
- TEXT-ONLY: mean 1588 km and median 50 km, 61% of Acc@161
- NON-GEO: mean 800 km and median 25 km, 80% of Acc@161 

**User home geolocation prediction task**
- TEXT-ONLY: mean 892 km and median 31 km, 74% of Acc@161
- NON-GEO: mean 567 km and median 26 km, 82% of Acc@161 

### Model Architecture and Objective

Implemented wrapper layer of liner regression with a custom number of output variables that operates with classification token generated by the base BERT model.

#### Hardware

NVIDIA GeForce GTX 1080 Ti

#### Software

Python IDE

## Model Card Contact

lutsai.k@gmail.com