Spaces:
Runtime error
Runtime error
Update text.py
Browse files
text.py
CHANGED
|
@@ -14,9 +14,9 @@ The underlying engines for the Abstractive part are transformer based model BART
|
|
| 14 |
|
| 15 |
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).
|
| 16 |
"""
|
| 17 |
-
|
| 18 |
sum_app_text_tab_2= """
|
| 19 |
-
|
| 20 |
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:
|
| 21 |
|
| 22 |
|
|
|
|
| 14 |
|
| 15 |
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).
|
| 16 |
"""
|
| 17 |
+
|
| 18 |
sum_app_text_tab_2= """
|
| 19 |
+
##
|
| 20 |
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:
|
| 21 |
|
| 22 |
|