Update README.md

README.md

@@ -23,7 +23,7 @@ tags:
 ## Model Description
 
 This model consists of a fine-tuned version of google-bert/bert-base-cased for a propaganda detection task. It is effectively a binary classifier, determining wether propaganda is present in the output string.
-This model was created by [`Identrics`](https://identrics.ai/), in the scope of the Wasper project.
+This model was created by [`Identrics`](https://identrics.ai/), in the scope of the WASPer project.
 
 
 ## Uses
@@ -55,14 +55,22 @@ print(output.logits)
 The training datasets for the model consist of a balanced set totaling 840 English examples that include both propaganda and non-propaganda content. These examples are collected from a variety of traditional media and social media sources, ensuring a diverse range of content. Aditionally, the training dataset is enriched with AI-generated samples. The total distribution of the training data is shown in the table below: 
 
 
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/66741cdd8123010b8f63f965/KyUIrMGWmmpnE67WZeQaN.png)
 
+The model was then tested on a smaller evaluation dataset, achieving an f1 score of 0.807.
 
-The model was then tested on a smaller evaluation dataset, achieving an f1 score of 0.807. The evaluation dataset is distributed as such:
 
+## Citation
 
+If you find our work useful, please consider citing WASPer:
 
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/66741cdd8123010b8f63f965/5MOK5L7Tq9Ff64t0rPo17.png)
+```
+@article{bai2024longwriter,
+  title={LongWriter: Unleashing 10,000+ Word Generation from Long Context LLMs}, 
+  author={Yushi Bai and Jiajie Zhang and Xin Lv and Linzhi Zheng and Siqi Zhu and Lei Hou and Yuxiao Dong and Jie Tang and Juanzi Li},
+  journal={arXiv preprint arXiv:2408.07055},
+  year={2024}
+}
+```