Update README.md

README.md

@@ -7,43 +7,10 @@ tags:
 pipeline_tag: text-classification
 ---
 
-# /var/folders/qg/vmj6zq4s7hb2pbkp3b8kstvh0000gn/T/tmp20v_99_o/fhamborg/newsframes-aff
+# NewsFrames classifier
 
-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:
+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.
 
-1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
-2. Training a classification head with features from the fine-tuned Sentence Transformer.
+## Acknowledgements
 
-## Usage
-
-To use this model for inference, first install the SetFit library:
-
-```bash
-python -m pip install setfit
-```
-
-You can then run inference as follows:
-
-```python
-from setfit import SetFitModel
-
-# Download from Hub and run inference
-model = SetFitModel.from_pretrained("/var/folders/qg/vmj6zq4s7hb2pbkp3b8kstvh0000gn/T/tmp20v_99_o/fhamborg/newsframes-aff")
-# Run inference
-preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
-```
-
-## BibTeX entry and citation info
-
-```bibtex
-@article{https://doi.org/10.48550/arxiv.2209.11055,
-doi = {10.48550/ARXIV.2209.11055},
-url = {https://arxiv.org/abs/2209.11055},
-author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
-keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
-title = {Efficient Few-Shot Learning Without Prompts},
-publisher = {arXiv},
-year = {2022},
-copyright = {Creative Commons Attribution 4.0 International}
-}
-```
+This work would not have been possible without the contributions by [Tilman Hornung](t1h0), Kim Heinser, and our team of student research assistants.