Add links to TAL blogs

app.py

@@ -81,11 +81,22 @@ gen_image_style = gr.Textbox(label="Image style", lines=1)
 # Layout and text around the app
 title='Beatles lyrics generator'
 description="<p style='text-align: center'>We've fine-tuned multiple language models on lyrics from The Beatles to generate Beatles-like text. Below are the results we obtained fine-tuning a GPT Neo model. After generation a title is generated using <a href='https://huggingface.co/czearing/story-to-title' target='_blank'>this model</a>. On top we use the generated title to suggest an album cover using <a href='https://huggingface.co/CompVis/stable-diffusion-v1-4' target='_blank'>Stable Diffusion 1.4</a>. Give it a try!</p>"
-article="""<p style='text-align: left'>These text generation models that output Beatles-like text were created by data scientists working for <a href='https://cmotions.nl/' target="_blank">Cmotions.</a>  
-        We tried several text generation models that we were able to load in Colab: a general <a href='https://huggingface.co/gpt2-medium' target='_blank'>GPT2-medium</a> model, the Eleuther AI small-sized GPT model <a href='https://huggingface.co/EleutherAI/gpt-neo-125M' target='_blank'>GPT-Neo</a> and the new kid on the block build by the <a href='https://bigscience.notion.site/BLOOM-BigScience-176B-Model-ad073ca07cdf479398d5f95d88e218c4' target='_blank'>Bigscience</a> initiative <a href='https://huggingface.co/bigscience/bloom-560m' target='_blank'>BLOOM 560m</a>. 
-        Further we've put together a <a href='https://huggingface.co/datasets/cmotions/Beatles_lyrics' target='_blank'> Huggingface dataset</a> containing all known lyrics created by The Beatles. Keep an eye on this <a href='https://www.theanalyticslab.nl/blogs/' target='_blank'>location </a> where we will publish several blogs on the creation of these models and their evaluation.
-        The default output contains 100 tokens and has a repetition penalty of 1.0. 
-         </p>"""
+article="""<p style='text-align: left'>These text generation models that output Beatles-like text were created by data
+    scientists working for <a href='https://cmotions.nl/' target="_blank">Cmotions.</a>
+    We tried several text generation models that we were able to load in Colab: a general <a
+        href='https://huggingface.co/gpt2-medium' target='_blank'>GPT2-medium</a> model, the Eleuther AI small-sized GPT
+    model <a href='https://huggingface.co/EleutherAI/gpt-neo-125M' target='_blank'>GPT-Neo</a> and the new kid on the
+    block build by the <a
+        href='https://bigscience.notion.site/BLOOM-BigScience-176B-Model-ad073ca07cdf479398d5f95d88e218c4'
+        target='_blank'>Bigscience</a> initiative <a href='https://huggingface.co/bigscience/bloom-560m'
+        target='_blank'>BLOOM 560m</a>.
+    Further we've put together a <a href='https://huggingface.co/datasets/cmotions/Beatles_lyrics' target='_blank'>
+        Huggingface dataset</a> containing all known lyrics created by The Beatles. A general blog on building this text generator can be found <a
+        href='https://www.theanalyticslab.nl/building-a-beatles-lyrics-generator/' target='_blank'>here. </a> If you are interested in how we evaluated the results of these models <a
+        href='https://www.theanalyticslab.nl/how-to-evaluate-a-text-generation-model-strengths-and-limitations-of-popular-evaluation-metrics/' target='_blank'>this blog</a> will enlighten you. Finally we also wrote on our experience to showcase the results of our model
+        by using HF spaces, details can be found <a href='https://www.theanalyticslab.nl/how-to-showcase-your-demo-on-a-hugging-face-space/' target='_blank'>here. </a>
+    The default output contains 100 tokens and has a repetition penalty of 1.0.
+</p>"""
 css = """
         .gr-button-primary {
           text-indent: -9999px;