Update README.md

README.md

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 ## Model Description
 
-This transformer-based model is designed to extrapolate affective norms for English words, including metrics such as valence, arousal, dominance, concreteness, and age of acquisition. It has been finetuned from the ERNIE 2.0 model, 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 English words, including metrics such as valence, arousal, dominance, concreteness, and age of acquisition. It has been finetuned from the ERNIE 2.0 model, 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 ANEW corpus (Bradley & Lang, 1999) and the corpus collected by Warriner et al. (2013) [ https://doi.org/10.3758/s13428-012-0314-x]. The ANEW corpus consists of 1030 words, while Warriner's dataset includes 13,915 words rated on various emotional and semantic dimensions. The combined dataset was split into training, validation, and test sets in an 8:1:1 ratio.
+The model was trained on the ANEW corpus (Bradley & Lang, 1999) and the corpus collected by Warriner et al. (2013) [ https://doi.org/10.3758/s13428-012-0314-x ]. The ANEW corpus consists of 1030 words, while Warriner's dataset includes 13,915 words rated on various emotional and semantic dimensions. The combined dataset was split into training, validation, and test sets in an 8:1:1 ratio.
 
 ## Performance