Update README.md

README.md

@@ -1,27 +1,18 @@
 ---
-license: apache-2.0
-tags:
-- setfit
-- sentence-transformers
-- text-classification
-- populism
-- politics
-- Populismus
 pipeline_tag: text-classification
-language:
-- de
-library_name: sentence-transformers
 ---
 
 # PopFit
 
-This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for detecting German-language populism. It is a binary classification model that was trained using diverse set of German-language texts, with a particular focus  on news data. 
+This is a [SetFit model](https://github.com/huggingface/setfit) for German-language populism detection in news and media content. It was created as part of master thesis at the Hochschule für Politik @TUM. 
 
-The model has been trained using an efficient few-shot learning technique that involves:
+The model is based on [deutsche-telekom/gbert-large-paraphrase-cosine](https://huggingface.co/deutsche-telekom/gbert-large-paraphrase-cosine) and has been trained using an efficient few-shot learning technique that involves:
 
 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.
 
+The model generates binary classification labels with 0 as the `non-populist` and 1 as the `populist` class. 
+
 ## Usage
 
 To use this model for inference, first install the SetFit library:
@@ -39,20 +30,4 @@ from setfit import SetFitModel
 model = SetFitModel.from_pretrained("baunef/PopFit")
 # Run inference
 preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
-```
-
-## BibTeX entry and citation info
-
-SetFit:
-```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}
-}
 ```