Update app.py

app.py

@@ -153,19 +153,19 @@ if __name__ == "__main__":
 
     with demo:
 
-        gr.Markdown("#Automatic summarization of biomedical research papers with neural abstractive methods into a long and comprehensive synopsis or extreme TLDR summary version")
+        gr.Markdown("# Automatic summarization of biomedical research papers with neural abstractive methods into a long and comprehensive synopsis or extreme TLDR summary version")
         gr.Markdown(
             "A rather simple demo using an ad-hoc fine-tuned LongT5 or LED model to summarize long biomedical articles (or any scientific text related to the biomedical domain) into a detailed or extreme TLDR version."
         )
         with gr.Column():
 
-            gr.Markdown("## Load Inputs & Select Parameters")
+            gr.Markdown("## Load Text Inputs & Select Generation Parameters")
             gr.Markdown(
-                "Enter text below in the text area. The text will be summarized [using the selected parameters](https://huggingface.co/blog/how-to-generate). Optionally load an example below or upload a file."
+                "Enter text below in the text area. The text will be summarized [using the selected text generation parameters](https://huggingface.co/blog/how-to-generate). Optionally load an available example below or upload a file."
             )
             with gr.Row():
                 model_size = gr.Radio(
-                    choices=["tldr", "sumpubmed"], label="Model Variant", value="large"
+                    choices=["tldr", "sumpubmed"], label="Model Variant", value="sumpubmed"
                 )
                 num_beams = gr.Radio(
                     choices=[2, 3, 4],
@@ -229,7 +229,7 @@ if __name__ == "__main__":
         with gr.Column():
             gr.Markdown("## Generate Summary")
             gr.Markdown(
-                "Summary generation should take approximately 1-2 minutes for most settings."
+                "Summary generation should take approximately less than 2 minutes for most settings."
             )
             summarize_button = gr.Button(
                 "Summarize!",
@@ -251,9 +251,9 @@ if __name__ == "__main__":
             gr.Markdown("---")
 
         with gr.Column():
-            gr.Markdown("## About the Model")
+            gr.Markdown("## About the Models")
             gr.Markdown(
-                "- [This model](https://huggingface.co/pszemraj/led-large-book-summary) is a fine-tuned checkpoint of [allenai/led-large-16384](https://huggingface.co/allenai/led-large-16384) on the [BookSum dataset](https://arxiv.org/abs/2105.08209).The goal was to create a model that can generalize well and is useful in summarizing lots of text in academic and daily usage."
+                "- [Blaise-g/longt5_tglobal_large_sumpubmed](https://huggingface.co/Blaise-g/longt5_tglobal_large_sumpubmed) is a fine-tuned checkpoint of [Stancld/longt5-tglobal-large-16384-pubmed-3k_steps](https://huggingface.co/Stancld/longt5-tglobal-large-16384-pubmed-3k_steps) on the [SumPubMed dataset](https://aclanthology.org/2021.acl-srw.30/). [Blaise-g/longt5_tglobal_large_scitldr](https://huggingface.co/Blaise-g/longt5_tglobal_large_scitldr) is a fine-tuned checkpoint of [Blaise-g/longt5_tglobal_large_sumpubmed](https://huggingface.co/Blaise-g/longt5_tglobal_large_sumpubmed) on the [Scitldr dataset](https://arxiv.org/abs/2004.15011). The goal was to create two models capable of handling the complex information contained in long biomedical documents and subsequently producing scientific summaries according to one of the two possible levels of conciseness: 1) A long explanatory synopsis that retains the majority of domain-specific language used in the original source text. 2)A one sentence long, TLDR style summary."
             )
             gr.Markdown(
                 "- The two most important parameters-empirically-are the `num_beams` and `token_batch_length`. However, increasing these will also increase the amount of time it takes to generate a summary. The `length_penalty` and `repetition_penalty` parameters are also important for the model to generate good summaries."