Update README.md

README.md

@@ -429,7 +429,7 @@ model-index:
   <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
 </a>
 
-The Longformer Encoder-Decoder (LED) for Narrative-Esque Long Text Summarization is a model I developed, designed to condense extensive technical, academic, and narrative content. 
+The Longformer Encoder-Decoder (LED) for Narrative-Esque Long Text Summarization is a model I fine-tuned, designed to condense extensive technical, academic, and narrative content in a fairly generalizable banner. 
 
 ## Key Features and Use Cases
 
@@ -438,7 +438,7 @@ The Longformer Encoder-Decoder (LED) for Narrative-Esque Long Text Summarization
 - High capacity: Handles up to 16,384 tokens per batch.
 - 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 ~96 tokens due to inference timeout constraints. For better results, use the Python approach detailed below.
+> **Note:** The API widget has a max length of ~96 tokens due to inference timeout constraints. 
 
 ## Training Details
 
@@ -448,7 +448,7 @@ Model checkpoint: [`pszemraj/led-base-16384-finetuned-booksum`](https://huggingf
 
 ## Other Related Checkpoints
 
-Apart from the LED-based model, I have also fine-tuned other models on `kmfoda/booksum`:
+This model is the smallest/fastest booksum-tuned model I have worked on. If you're looking for higher quality summaries, check out:
 
 - [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)
@@ -524,6 +524,6 @@ print(f"summary: {out_str}")
 
 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.
+For detailed explanations and documentation, check the [README](https://github.com/pszemraj/textsum) or the [wiki](https://github.com/pszemraj/textsum/wiki)
 
 ---