Update README.md

README.md

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 # Longformer Encoder-Decoder (LED) for Narrative-Esque Long Text Summarization
 
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+ [![colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/gist/pszemraj/36950064ca76161d9d258e5cdbfa6833/led-base-demo-token-batching.ipynb)
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 - **What:** This is the (current) result of the quest for a summarization model that condenses technical/long information down well _in general, academic and narrative usage
 - **Use cases:** long narrative summarization (think stories - as the dataset intended), article/paper/textbook/other summarization, technical:simple summarization. 
   - Models trained on this dataset tend to also _explain_ what they are summarizing, which IMO is awesome.
+  - Works well on lots of text, and can hand 16384 tokens/batch.
+  - See examples in Colab demo linked above, or try the [demo on Spaces](https://huggingface.co/spaces/pszemraj/summarize-long-text)
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+> Note: the API is set to generate a max of 64 tokens for runtime reasons, so the summaries may be truncated (depending on the length of input text). For best results use python as below.
 
-- works well on lots of text, and can hand 16384 tokens/batch.
 
 ## About