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ariannap22/
roberta_finetuned_multilabel_unions

Name: roberta_finetuned_multilabel_unions
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Multi-Label RoBERTa Classifier for Union Communication Frames

This model is a multi-label classifier fine-tuned on U.S. labor union Facebook posts. It predicts the following communication frames:

Diagnostic
Prognostic
Motivational
Community
Engagement
Political Endorsement

The model was trained to automatically label union posts according to these categories, supporting research on social media discourse around labor unions. The predicted outcome is a list of 6 binary labels of the kind [0, 0, 1, 0, 0, 0]. Each binary label correspond to a discourse frame in the order presented above.

More details can be found in the paper "Framing Unionization on Facebook: Communication around Representation Elections in the United States".

Model Details

Base model: RoBERTa-base
Task: Multi-label text classification
Domain: Labor union Facebook posts
Framework: Simple Transformers

Usage

python

from simpletransformers.classification import MultiLabelClassificationModel
import torch
import pandas as pd

def main():

    cuda_available = torch.cuda.is_available()

    model_args = {
        "process_count": 1,  # disables multiprocessing
    }

    model = MultiLabelClassificationModel(
        "roberta",
        "ariannap22/roberta_finetuned_multilabel_unions",
        args=model_args,
        use_cuda=cuda_available
    )

    # Example inference
    texts = [
        "We successfully won the contract vote!",
        "Join us for the upcoming rally to support labor rights."
    ]

    predictions, raw_outputs = model.predict(texts)

    print(predictions)

if __name__ == "__main__":
    main()