Update README.md

README.md

@@ -433,12 +433,12 @@ The Longformer Encoder-Decoder (LED) for Narrative-Esque Long Text Summarization
 
 ## Key Features and Use Cases
 
-- Ideal for summarizing long narratives, articles, papers, textbooks, and other technical documents.
-- Trained to also explain the summarized content, offering insightful output.
+- Ideal for summarizing long narratives, articles, papers, textbooks, and other documents.
+  - the sparknotes-esque style leads to 'explanations' in the summarized content, offering insightful output.
 - High capacity: Handles up to 16,384 tokens per batch.
-- Live demo available: [Colab demo](https://colab.research.google.com/gist/pszemraj/36950064ca76161d9d258e5cdbfa6833/led-base-demo-token-batching.ipynb) and [demo on Spaces](https://huggingface.co/spaces/pszemraj/summarize-long-text).
+- demos: try it out in the notebook linked above or in the [demo on Spaces](https://huggingface.co/spaces/pszemraj/summarize-long-text)
 
-> **Note:** The API is configured to generate a maximum of 64 tokens due to runtime constraints. For optimal results, use the Python approach detailed below.
+> **Note:** The API is configured to generate a maximum of ~96 tokens due to inference timeout constraints. For better results, use the Python approach detailed below.
 
 ## Training Details
 
@@ -446,16 +446,14 @@ The model was trained on the BookSum dataset released by SalesForce, which leads
 
 Model checkpoint: [`pszemraj/led-base-16384-finetuned-booksum`](https://huggingface.co/pszemraj/led-base-16384-finetuned-booksum). 
 
-For comparison, all generation parameters for the API have been kept consistent across versions.
-
 ## Other Related Checkpoints
 
 Apart from the LED-based model, I have also fine-tuned other models on `kmfoda/booksum`:
 
-- [Long-T5-Global-Base](https://huggingface.co/pszemraj/long-t5-tglobal-base-16384-book-summary)
+- [Long-T5-tglobal-base](https://huggingface.co/pszemraj/long-t5-tglobal-base-16384-book-summary)
 - [BigBird-Pegasus-Large-K](https://huggingface.co/pszemraj/bigbird-pegasus-large-K-booksum)
 - [Pegasus-X-Large](https://huggingface.co/pszemraj/pegasus-x-large-book-summary)
-- [Long-T5-Global-XL](https://huggingface.co/pszemraj/long-t5-tglobal-xl-16384-book-summary)
+- [Long-T5-tglobal-XL](https://huggingface.co/pszemraj/long-t5-tglobal-xl-16384-book-summary)
 
 There are also other variants on other datasets etc on my hf profile, feel free to try them out :)
 
@@ -524,6 +522,8 @@ out_str = summarizer.summarize_string(long_string)
 print(f"summary: {out_str}")
 ```
 
-Currently implemented interfaces include a Python API, a Command-Line Interface (CLI), and a shareable demo application. For detailed explanations and documentation, check the README or the wiki.
+Currently implemented interfaces include a Python API, a Command-Line Interface (CLI), and a shareable demo/web UI. 
+
+For detailed explanations and documentation, check the [README](https://github.com/pszemraj/textsum) or the [wiki](https://github.com/pszemraj/textsum/wiki.
 
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