|
--- |
|
widget: |
|
- text: The food was super tasty, I enjoyed every bite. |
|
language: |
|
- en |
|
library_name: transformers |
|
pipeline_tag: text-classification |
|
--- |
|
|
|
# Model Card for Model ID |
|
|
|
<!-- Provide a quick summary of what the model is/does. --> |
|
|
|
This model performs sentiment analysis on user reviews, rating them on a scale from 1 (Very Bad) to 5 (Very Good). |
|
|
|
# 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 user ratings from 1 to 5, where: |
|
|
|
5 = Very Good |
|
|
|
4 = Good |
|
|
|
3 = Neutral |
|
|
|
2 = Bad |
|
|
|
1 = Very Bad |
|
|
|
Model type: BERT-based sentiment analysis 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 sentiment of user reviews for public places and points of interest, providing a rating from 1 to 5. |
|
|
|
# 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. |
|
|
|
## Recommendations |
|
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
|
Users should be aware of potential biases and limitations in the model’s performance, particularly when applied to reviews from different regions or contexts. |
|
|
|
# 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-User-Review-Rating") |
|
|
|
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. --> |
|
|
|
The subjective performance of the model is better than the metrices, because it was evaluated regarding meeting the exact correct prediction, so if it reflects 4 instead of 5 it was counted as wrong prediciton. Even though the prediction was way better than predicting 1 for a 5 review. |
|
|
|
Test Accuracy: 0.714 |
|
Test F1 Score: 0.695 |
|
Test Loss: 0.698 |
|
Test Recall: 0.714 |
|
|
|
# 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 |
|
|
|
|
