Update README.md

README.md

@@ -6,7 +6,7 @@ This model was trained on ~25k heterogeneous manually annotated sentences (📚
 
 🗃 **Dataset**
 
-The dataset (📚 Stab et al. 2018) consists of **ARGUMENTS** (\~11k) that either support or oppose a topic if it includes a relevant reason for supporting or opposing the topic, or as a **NON-ARGUMENT** (\~14k) if it does not include reasons. The authors focus on controversial topics, i.e., topics that include an obvious polarity to the possible outcomes and compile a final set of eight controversial topics: _abortion, school uniforms, death penalty, marijuana legalization, nuclear energy, cloning, gun control, and minimum wage_.
+The dataset (📚 Stab et al. 2018) consists of **ARGUMENTS** (\\~11k) that either support or oppose a topic if it includes a relevant reason for supporting or opposing the topic, or as a **NON-ARGUMENT** (\\~14k) if it does not include reasons. The authors focus on controversial topics, i.e., topics that include an obvious polarity to the possible outcomes and compile a final set of eight controversial topics: _abortion, school uniforms, death penalty, marijuana legalization, nuclear energy, cloning, gun control, and minimum wage_.
 
 | TOPIC | ARGUMENT | NON-ARGUMENT |
 |----|----|----|
@@ -54,4 +54,7 @@ The model can be a starting point to dive into the exciting area of argument min
 
 Enjoy and stay tuned! 🚀
 
-📚Stab et al. (2018): Cross-topic Argument Mining from Heterogeneous Sources. [LINK](https://www.aclweb.org/anthology/D18-1402/).
+📚Stab et al. (2018): Cross-topic Argument Mining from Heterogeneous Sources. [LINK](https://www.aclweb.org/anthology/D18-1402/).
+
+widget:
+ - text: "Mi estas viro kej estas tago varma."