Update README.md

README.md

@@ -19,6 +19,7 @@ datasets:
 - samsum
 - scitldr/AIC
 - billsum
+- TLDR
 metrics:
 - rouge
 widget:
@@ -133,15 +134,15 @@ inference:
     num_beams: 4
 ---
 
-# Longformer Encoder-Decoder (LED) for Narrative-Esque Long Text Summarization
+# Multi-purpose Summarizer (Fine-tuned google/flan-t5-xl (3B) on several Summarization datasets)
 
 <a href="https://colab.research.google.com/gist/pszemraj/3eba944ddc9fc9a4a1bfb21e83b57620/summarization-token-batching.ipynb">
   <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
 </a>
 
-A fine-tuned version of [allenai/led-large-16384](https://huggingface.co/allenai/led-large-16384) on the `BookSum` dataset.
+A fine-tuned version of [google/flan-t5-xl](https://huggingface.co/google/flan-t5-xl) on various summarization datasets (xsum, wikihow, cnn_dailymail/3.0.0, samsum, scitldr/AIC, billsum, TLDR)
 
-Goal: a model that can generalize well and is useful in summarizing long text in academic and daily usage. The result works well on lots of text and can handle 16384 tokens/batch (_if you have the GPU memory to handle that_)
+Goal: a model that can be used for a general-purpose summarizer for academic and general usage. Control over the type of summary can be given by varying the instruction prepended to the source document. The result works well on lots of text, although trained with a max source length of 512 tokens and 150 max summary length. 
 
  - See the Colab demo linked above or try the [demo on Spaces](https://huggingface.co/spaces/pszemraj/summarize-long-text)