Bert-Place-Type

Model Card for Model ID

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

This model predicts the type of a place (e.g. restaurant, hotel, park) based on the text of a user review.

E.g.

'I enjoyed the food, it was very delicious' -> 'Restaurants'

'I liked the exhibition, very inspiring' -> 'Museums and Galleries'

Model Details

Model Description

The Bert-User-Review-Rating model is trained on a dataset of 1,300,000 reviews of public places and points of interest. It is capable of classifying the type of a place based on a user review into one of the following categories:

0: 'Specialty Food Stores', 1: 'Hotels and Inns', 2: 'Schools and Universities', 3: 'Shopping mall', 4: 'Museums and Galleries', 5: 'Restaurants', 6: 'Parks', 7: 'Shops', 8: 'Cafes and Coffee Shops', 9: 'Cultural Institutions', 10: 'Places of Worship', 11: 'Leisure and Amusement', 12: 'Tourist Attractions', 13: 'Medical Services', 14: 'Social Services', 15: 'Food Courts', 16: 'Sports and Fitness', 17: 'Outdoor Activities', 18: 'Training and Development', 19: 'Bars and Pubs', 20: 'Industrial and Commercial', 21: 'Wellness Services', 22: 'Pets Services', 23: 'Public Transit', 24: 'Performing Arts', 25: 'Vehicle Services', 26: 'Other Lodging', 27: 'Professional Services', 28: 'Government Services', 29: 'Religious Services', 30: 'Travel Services'

Model type: BERT-based model

Language(s) (NLP): English

Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

The model can be used directly to classify the type of a place based on a user review.

Bias, Risks and Limitations

The model may reflect biases present in the training data, such as cultural or regional biases, as training data reflects public places in Singapore.

How to Get Started with the Model

Use the code below to get started with the model.

Use a pipeline as a high-level helper

from transformers import pipeline

pipe = pipeline("text-classification", model="mekes/Bert-Place-Type")

result = pipe("The food was super tasty, I enjoyed every bite.")

print(result)

Metrics

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

Eval Accuracy: 0.753

Eval F1 Score: 0.741

Eval Recall: 0.753

Environmental Impact

Carbon emissions were estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019)

Calculations were done with Nvidia RTX 3090 instead of the used Nvidia RTX 4090.

For one training run it emmited approximately 1,5 kg CO2