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# BEpaRTy

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on 2.5 million tweets from 825 U.S. congressional politicians.
It is capable of predicting the political orientation of tweets. The model's testing accuracy is 89.54%, with an F1 score of 0.8939. 
This model serves as the initial step in the paper titled "A Two-Step Method to Classify Political Partisanship Using a Deep Learning Model."


## Model description


## Intended uses & limitations

This model is capable of predicting the political orientation of tweets. It classifies tweets into 0 (Democratic) and 1 (Republican). 

## Training and evaluation data

2.5 million tweets from 825 U.S. congressional politicians.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- optimizer: default
- training_precision: default

### Training results

On the training dataset (80% of the dataset):

              precision    recall  f1-score   support

           0     0.9655    0.9782    0.9718   1001625
           1     0.9779    0.9651    0.9714   1002311

    accuracy                         0.9716   2003936
   macro avg     0.9717    0.9716    0.9716   2003936
weighted avg     0.9717    0.9716    0.9716   2003936

On the testing dataset (20% of the dataset):
              precision    recall  f1-score   support

           0     0.8852    0.9091    0.8970    250835
           1     0.9063    0.8818    0.8939    250149

    accuracy                         0.8954    500984
   macro avg     0.8957    0.8954    0.8954    500984
weighted avg     0.8957    0.8954    0.8954    500984



### Framework versions

- Transformers 4.26.1
- TensorFlow 2.6.5
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "BEpaRTy", "results": []}]}
transformers tf bert text-classification generated_from_keras_callback license:apache-2.0 autotrain_compatible endpoints_compatible region:us