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| | # Model Card for Model Geo-BERT-multilingual |
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| | <!-- Provide a quick summary of what the model is/does. --> |
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| | 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). |
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| | ## Model Details |
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| | Number of predicted points: 5 |
| | Custom transformers pipeline and result visualization: https://github.com/K4TEL/geo-twitter/tree/predict |
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| | ### Model Description |
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| | <!-- Provide a longer summary of what this model is. --> |
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| | 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. |
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| | - **Developed by:** Kateryna Lutsai |
| | - **Model type:** regression |
| | - **Language(s) (NLP):** multilingual |
| | - **Finetuned from model:** bert-base-multilingual-cased |
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| | ### Model Sources |
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| | <!-- Provide the basic links for the model. --> |
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| | - **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 |
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| | ## Uses |
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| | <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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| | Geo-tagging of Big data |
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| | ### Direct Use |
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| | <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> |
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| | Per-tweet geolocation prediction |
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| | ### Out-of-Scope Use |
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| | <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> |
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| | Per-tweet geolocation prediction without "user" metadata is expected to show lower accuracy of predictions. |
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| | ## Bias, Risks, and Limitations |
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| | <!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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| | Risk for unethical use on the basis of data that is not publicly available. |
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| | The limitation of text length is dictated by the BERT-based model's capacity of 500 tokens (words). |
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| | ### How to Get Started with the Model |
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| | Use the code below to get started with the model: |
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| | https://github.com/K4TEL/geo-twitter/tree/predict |
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| | A short startup guide is given in the repository branch description. |
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| | ## Training Details |
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| | ### Training Data |
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| | <!-- 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. --> |
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| | The Twitter dataset contained tweets with their text content, metadata ("user" and "place") context, and geolocation coordinates. |
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| | ### Training Procedure |
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| | <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the 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 |
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| | #### Training Hyperparameters |
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| | - **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 |
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| | ## Evaluation |
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| | <!-- 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 |
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| | ### Testing Data, Factors & Metrics |
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| | #### Testing Data |
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| | <!-- This should link to a Data Card if possible. --> |
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| | Worldwide dataset of tweets with TEXT-ONLY and NON-GEO features |
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| | #### Metrics |
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| | <!-- These are the evaluation metrics being used, ideally with a description of why. --> |
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| | 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 |
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| | ### Results |
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| | **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 |
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| | **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 |
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| | ### Model Architecture and Objective |
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| | Implemented wrapper layer of liner regression with a custom number of output variables that operates with classification token generated by the base BERT model. |
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| | #### Hardware |
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| | NVIDIA GeForce GTX 1080 Ti |
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| | #### Software |
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| | Python IDE |
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| | ## Model Card Contact |
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| | lutsai.k@gmail.com |
