Create README.md

README.md

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+---
+language:
+- en
+library_name: transformers
+tags:
+- map
+- reviews
+- public places
+---
+
+
+# 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.
+
+## Downstream Use [optional]
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+The model can be fine-tuned for user-review related tasks
+
+# 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
+