Update README.md

README.md

@@ -20,7 +20,7 @@ This model classifies the valence of rhetorical appeals by politicians to groups
 
 ### Model Description
 
-This model adapts [Mike Burnham](https://huggingface.co/mlburnham/deberta-v3-large-polistance-affect-v1.1)'s zero shot model for political stance detection, which is itself an adaptation of [Moritz Laurer](https://huggingface.co/MoritzLaurer/deberta-v3-large-zeroshot-v1.1-all-33)'s zero shot model for classifying political texts. It is trained for the more specific use of classifying the valence of rhetorical appeals by politicians to groups ("group appeals") in political speech. The model takes in sentences that are formatted so as to mention the sender/speaker and the group mentioned (i.e. the 'dyad') of the form: "Politician from <party> mentioning a group (<group>): '<text>'". It returns the probability that the speaker is making a positive appeal to the group. 
+This model adapts [Mike Burnham](https://huggingface.co/mlburnham/deberta-v3-large-polistance-affect-v1.1)'s zero shot model for political stance detection, which is itself an adaptation of [Moritz Laurer](https://huggingface.co/MoritzLaurer/deberta-v3-large-zeroshot-v1.1-all-33)'s zero shot model for classifying political texts. It is trained for the more specific use of classifying the valence of rhetorical appeals by politicians to groups ("group appeals") in political speech. The model takes in sentences that are formatted so as to mention the sender/speaker and the group mentioned (i.e. the 'dyad') of the form: "Politician from {party} mentioning a group ({group}): '{text}'". It returns the probability that the speaker is making a positive appeal to the group. 
 
 - **Developed by:** Christoffer H. Dausgaard & Frederik Hjorth
 - **Model type:** Fine-tuned DeBERTa-model