Edit model card

This RoBERTa-based model can classify expressed purchase intentions in English language text in 2 classes:

  • purchase intention 🤩
  • no purchase intention 😐

The model was fine-tuned on 2,000 manually annotated social media posts. The hold-out accuracy is 95% (vs. a balanced 50% random-chance baseline). For details on the training approach see Web Appendix F in Hartmann et al. (2021).

Application

from transformers import pipeline
classifier = pipeline("text-classification", model="j-hartmann/purchase-intention-english-roberta-large", return_all_scores=True)
classifier("I want this!")
Output:
[[{'label': 'no', 'score': 0.0014553926885128021},
  {'label': 'yes', 'score': 0.9985445737838745}]]

Reference

Please cite this paper when you use our model. Feel free to reach out to jochen.hartmann@tum.de with any questions or feedback you may have.

@article{hartmann2021,
  title={The Power of Brand Selfies},
  author={Hartmann, Jochen and Heitmann, Mark and Schamp, Christina and Netzer, Oded},
  journal={Journal of Marketing Research}
  year={2021}
}
Downloads last month
20
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.