Update text.py

text.py

@@ -8,14 +8,14 @@ sum_app_text_tab_1= """
                 
                 """
                 
-sum_app_text_tab_2= """
-                ## Abstractive vs Extractive
-                
-                The underlying engines for the Abstractive part are transformer based model BART, a sequence-to-sequence model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. The BART-model was pre-trained by KBLab/bart-base-swedish-cased (link) to learn general knowledge about language. Afterwards, the model was further fine-tuned on two labelled datasets that have been open-sourced:
-- Gabriel/cnn_daily_swe (link)
-- Gabriel/xsum_swe (link)
+sum_app_text_tab_2= """ ## Abstractive vs Extractive
+
+The underlying engines for the Abstractive part are transformer based model BART, a sequence-to-sequence model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. The BART-model was pre-trained by KBLab/bart-base-swedish-cased (link) to learn general knowledge about language. Afterwards, the model was further fine-tuned on two labelled datasets that have been open-sourced:
+
+- [Gabriel/xsum_swe](https://huggingface.co/datasets/Gabriel/xsum_swe)
+- [Gabriel/cnn_daily_swe](https://huggingface.co/datasets/Gabriel/cnn_daily_swe)
 
-To see more in depth regarding the training go to link.
+To see more in depth regarding the training go to model card: [Gabriel/bart-base-cnn-xsum-swe](https://huggingface.co/Gabriel/bart-base-cnn-xsum-swe).
 
 The core idea behind the training procedure is sequential adoption through transfer learning, i.e multiple phases for fine-tuning a pretrained model on different datasets. The figure below illustrates how the skill level of the model increases at each step: