The BiasCheck-RoBERTa model is a political bias detection model based on the RoBERTa architecture. It classifies news articles into three political bias categories: Left, Center, and Right. This model was trained on a curated dataset of articles from allsides available on kaggle, and it utilizes the RoBERTa-base model as the base architecture for text classification. The model provides a reliable way to identify political bias in news articles, helping users to assess the bias of the content they consume.
Base Model
The BiasCheck-RoBERTa model is based on the RoBERTa-base architecture, a robust transformer-based model that has been pre-trained on vast amounts of text data.
The model was evaluated using several performance metrics. Below are the key metrics:
Accuracy: 0.913
Precision: 0.914
Recall: 0.913
F1-Score: 0.913
Log Loss: 0.233
AUC-ROC: 0.986
Carbon Emission
Experiments were conducted using a private infrastructure, which has a carbon efficiency of 0.432 kgCO$_2$eq/kWh. A cumulative of 24 hours of computation was performed on hardware of type RTX 3080 (TDP of 320W).
Total emissions are estimated to be 3.32 kgCO2eq of which 0 percents were directly offset.
3.32 kgCO2eq is equivalent to
13.4 Km driven by an average ICE car
1.66 Kgs of coal burned
0.06 Tree seedlings sequesting carbon for 10 years
Installation
To use this model, you will need to install the following dependencies:
make a requirements.txt file and add the following dependencies
