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  pipeline_tag: text-classification
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  ---
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- # /var/folders/qg/vmj6zq4s7hb2pbkp3b8kstvh0000gn/T/tmpdk_aocux/fhamborg/newsframes-econ-bin
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- This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
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- 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
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- 2. Training a classification head with features from the fine-tuned Sentence Transformer.
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- ## Usage
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-
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- To use this model for inference, first install the SetFit library:
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-
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- ```bash
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- python -m pip install setfit
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- ```
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-
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- You can then run inference as follows:
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-
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- ```python
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- from setfit import SetFitModel
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-
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- # Download from Hub and run inference
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- model = SetFitModel.from_pretrained("/var/folders/qg/vmj6zq4s7hb2pbkp3b8kstvh0000gn/T/tmpdk_aocux/fhamborg/newsframes-econ-bin")
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- # Run inference
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- preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
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- ```
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-
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- ## BibTeX entry and citation info
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-
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- ```bibtex
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- @article{https://doi.org/10.48550/arxiv.2209.11055,
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- doi = {10.48550/ARXIV.2209.11055},
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- url = {https://arxiv.org/abs/2209.11055},
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- author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
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- keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
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- title = {Efficient Few-Shot Learning Without Prompts},
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- publisher = {arXiv},
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- year = {2022},
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- copyright = {Creative Commons Attribution 4.0 International}
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- }
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- ```
 
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  pipeline_tag: text-classification
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  ---
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+ # NewsFrames classifier
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+ This is one of a series of classifiers devised for automatically identifying universal framing dimensions. A paper on the underlying training dataset and the framing dimensions in particular is currently being written. This page will be updated once the paper is finished.
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+ ## Acknowledgements
 
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+ This work would not have been possible without the contributions by [Tilman Hornung](t1h0), Kim Heinser, and our team of student research assistants.