Update README.md

README.md

@@ -195,7 +195,14 @@ library_name: transformers
 ---
 
 
-# long-t5-tglobal-base-sci-simplify-elife
+# long-t5-tglobal-base-sci-simplify: elife subset
+
+
+Exploring how well long-document models trained on "lay summaries" of scientific papers generalize. 
+
+> A lay summary is a summary of a research paper or scientific study that is written in plain language, without the use of technical jargon, and is designed to be easily understood by non-experts.
+
+## Model description
 
 This model is a fine-tuned version of [google/long-t5-tglobal-base](https://huggingface.co/google/long-t5-tglobal-base) on the `pszemraj/scientific_lay_summarisation-elife-norm` dataset.
 
@@ -207,17 +214,33 @@ It achieves the following results on the evaluation set:
 - Rougelsum: 35.9333
 - Gen Len: 392.7095
 
-## Model description
-
-More information needed
 
 ## Intended uses & limitations
 
-More information needed
+- Ability to generalize outside of the dataset domain (pubmed/bioscience type papers) has to be evaluated.
+
+
+## Usage 
+
+It's recommended to usage this model with [beam search decoding](https://huggingface.co/docs/transformers/generation_strategies#beamsearch-decoding). If interested, you can also use the `textsum` util repo to have most of this abstracted out for you:
+
+
+```bash
+pip install -U textsum
+```
+
+```python
+from textsum.summarize import Summarizer
+
+summarizer = Summarizer('pszemraj/long-t5-tglobal-base-sci-simplify')
+text = "put the text you don't want to read here"
+summary = summarizer.summarize_string(text)
+print(summary)
+```
 
 ## Training and evaluation data
 
-The `elife` subset of the :lay summaries dataset. Refer to `pszemraj/scientific_lay_summarisation-elife-norm`.
+The `elife` subset of the :lay summaries dataset. Refer to `pszemraj/scientific_lay_summarisation-elife-norm`
 
 ## Training procedure