init app

app.py

@@ -32,8 +32,7 @@ description = """
     as introduced in <a href="https://arxiv.org/abs/2102.05918"></a><i>"Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision
     "</i>. ALIGN features a dual-encoder architecture with EfficientNet and BERT as its text and vision encoders, and learns to align visual and text representations with contrastive learning. 
     Unlike previous work, ALIGN leverages a massive noisy dataset and shows that the scale of the corpus can be used to achieve SOTA representations with a simple recipe.
-    \n\nALIGN is not open-sourced and the `kakaobrain/align-base` model used for this demo is based on the Kakao Brain implementation that follows the original paper. 
-    The model is trained on the open source [COYO](https://github.com/kakaobrain/coyo-dataset) dataset by the Kakao Brain team.
+    \n\nALIGN is not open-sourced and the `kakaobrain/align-base` model used for this demo is based on the Kakao Brain implementation that follows the original paper.  The model is trained on the open source [COYO](https://github.com/kakaobrain/coyo-dataset) dataset by the Kakao Brain team.
     To perform zero-shot image classification with ALIGN, upload an image and enter your candidate labels as free-form text separated by a comma followed by a space.</p>
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