Update README.md

README.md

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 ## Model Description
 
-This transformer-based model is designed to extrapolate affective norms for Polish words, including metrics such as valence, arousal, dominance, concreteness, age of acquisition, origin, significance, and imageability. It has been finetuned from the Polish RoBerta Model (https://github.com/sdadas/polish-roberta), enhanced with additional layers to predict the affective dimensions. This model was first released as a part of the publication: "Extrapolation of affective norms using transformer-based neural networks and its application to experimental stimuli selection." (Plisiecki, Sobieszek; 2023) [https://doi.org/10.3758/s13428-023-02212-3]
+This transformer-based model is designed to extrapolate affective norms for Polish words, including metrics such as valence, arousal, dominance, concreteness, age of acquisition, origin, significance, and imageability. It has been finetuned from the Polish RoBerta Model (https://github.com/sdadas/polish-roberta), enhanced with additional layers to predict the affective dimensions. This model was first released as a part of the publication: "Extrapolation of affective norms using transformer-based neural networks and its application to experimental stimuli selection." (Plisiecki, Sobieszek; 2023) [ https://doi.org/10.3758/s13428-023-02212-3 ]
 
 
 ## Training Data
 
-The model was trained on the Polish affective norms dataset by Imbir (2016) [https://doi.org/10.3389/fpsyg.2016.01081], which includes 4900 words rated by participants on various emotional and semantic dimensions. The dataset was split into training, validation, and test sets in an 8:1:1 ratio.
+The model was trained on the Polish affective norms dataset by Imbir (2016) [ https://doi.org/10.3389/fpsyg.2016.01081 ], which includes 4900 words rated by participants on various emotional and semantic dimensions. The dataset was split into training, validation, and test sets in an 8:1:1 ratio.
 
 ## Performance