Update README.md

README.md

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 ## Model Description
 
-This transformer-based model is designed to extrapolate affective norms for Spanish words, including metrics such as valence, arousal, concreteness, imageability, and familiarity. It has been finetuned from the "bert-base-spanish-wwm-cased" model (https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased), 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 Spanish words, including metrics such as valence, arousal, concreteness, imageability, and familiarity. It has been finetuned from the "bert-base-spanish-wwm-cased" model (https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased), 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 Spanish affective norms dataset by Redondo et al. (2007) [https://doi.org/10.3758/BF03193031], which includes 1400 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 Spanish affective norms dataset by Redondo et al. (2007) [ https://doi.org/10.3758/BF03193031 ], which includes 1400 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