Update README.md

README.md

@@ -12,7 +12,7 @@ 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 |
 |----|----|----|
@@ -58,4 +58,6 @@ Showing the **confusion matrix** using again the evaluation set:
 
 The model can only be a starting point to dive into the exciting field of argument mining. But be aware. An argument is a complex structure, with multiple dependencies. Therefore, the model may perform less well on different topics and text types not included in the training set.
 
-Enjoy and stay tuned! 🚀
+Enjoy and stay tuned! 🚀
+
+🐦 Twitter: [@chklamm](http://twitter.com/chklamm)