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 clinical psychology abstracts into two categories: 'positive results only' and 'mixed and negative results', and trained models using SciBERT.
+We annotated over 1,900 clinical psychology abstracts into two categories: 'positive results only' and 'mixed or 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).
@@ -26,7 +26,7 @@ Further information on documentation, code and data for the project "Publication
 ## Using the model on Huggingface
 The model can be used on Hugginface utilizing the "Hosted inference API" in the window on the right.
 Click 'Compute' to predict the class labels for an example abstract or an abstract inserted by yourself.
-The class label 'positive' corresponds to 'positive results only', while 'negative' represents 'mixed and negative results'.
+The class label 'positive' corresponds to 'positive results only', while 'negative' represents 'mixed or negative results'.
   
 ## Using the model for larger data
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