Update README.md

README.md

@@ -15,7 +15,7 @@ SciBERT text classification model for positive and negative results prediction i
 We annotated over 1,900 clinical psychology abstracts into two categories: 'positive results only' and 'mixed and negative results', and trained models using SciBERT.
 The SciBERT model was validated against one in-domain (clinical psychology) and two out-of-domain data sets comprising psychotherapy abstracts. We compared model performance with Random Forest and three further benchmarks: natural language indicators of result types, *p*-values, and abstract length.
 SciBERT outperformed all benchmarks and random forest in in-domain (accuracy: 0.86) and out-of-domain data (accuracy: 0.85-0.88). 
-Further information on documentation, code and data for the project "Publication Bias Research in Clincial Psychology Using Natural Language Processing" can be found on this [Github repository ](https://github.com/PsyCapsLock/PubBiasDetect).
+Further information on documentation, code and data for the project "Publication Bias Research in Clincial Psychology Using Natural Language Processing" can be found on this [GitHub repository](https://github.com/PsyCapsLock/PubBiasDetect).
 
 ## Using the Model on Huggingface
 The model can be used on Hugginface utilizing the "Hosted inference API" in the window on the right.