Update README.md

README.md

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 SciBERT text classification model for positive and negative results prediction in scientific abstracts of clinical psychology and psychotherapy. 
 
 ## Data
-We annotated over 1,900 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 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. Further information on documentation, code and data for the project "Publication Bias Research in Clincial Psychology Using Natural Language Processing" can be found on the Github repository [PubBiasDetect](https://github.com/PsyCapsLock/PubBiasDetect).
+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. Further information on documentation, code and data for the project "Publication Bias Research in Clincial Psychology Using Natural Language Processing" can be found on the Github repository [PubBiasDetect](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.