Update README.md

README.md

@@ -36,7 +36,7 @@ embeds_dropout_prob = 0.1
 We evaluated the extractive question answering performance on our GermanQuAD test set.
 Model types and training data are included in the model name. 
 For finetuning XLM-Roberta, we use the English SQuAD v2.0 dataset.
-The GELECTRA models are warm started on the German translation of SQuAD v1.1 and finetuned on \germanquad. 
+The GELECTRA models are warm started on the German translation of SQuAD v1.1 and finetuned on \\germanquad. 
 The human baseline was computed for the 3-way test set by taking one answer as prediction and the other two as ground truth.
 ![performancetable](https://lh3.google.com/u/0/d/1IFqkq8OZ7TFnGzxmW6eoxXSYa12f2M7O=w1970-h1546-iv1) 
 
@@ -56,4 +56,6 @@ Some of our work:
 - [Haystack](https://github.com/deepset-ai/haystack/)
 
 Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Website](https://deepset.ai)  
+[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
+
+By the way: [we're hiring!](https://apply.workable.com/deepset/)