add usage

README.md

@@ -4,13 +4,6 @@ datasets:
 - pszemraj/scientific_lay_summarisation-plos-norm
 language:
 - en
-pipeline_tag: summarization
-tags:
-- lay summaries
-- paper summaries
-- biology
-- medical
-library_name: transformers
 widget:
 - text: large earthquakes along a given fault segment do not occur at random intervals
     because it takes time to accumulate the strain energy for the rupture. The rates
@@ -174,15 +167,22 @@ widget:
   example_title: Richard & Mortimer
 - text: "The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building, and the tallest structure in Paris. Its base is square, measuring 125 metres (410 ft) on each side. During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest man-made structure in the world, a title it held for 41 years until the Chrysler Building in New York City was finished in 1930. It was the first structure to reach a height of 300 metres. Due to the addition of a broadcasting aerial at the top of the tower in 1957, it is now taller than the Chrysler Building by 5.2 metres (17 ft). Excluding transmitters, the Eiffel Tower is the second tallest free-standing structure in France after the Millau Viaduct."
   example_title: eiffel
-  parameters:
-    max_length: 64
-    min_length: 8
-    no_repeat_ngram_size: 3
-    early_stopping: true
-    repetition_penalty: 3.5
-    encoder_no_repeat_ngram_size: 4
-    length_penalty: 0.4
-    num_beams: 4
+parameters:
+  max_length: 64
+  min_length: 8
+  no_repeat_ngram_size: 3
+  early_stopping: true
+  repetition_penalty: 3.5
+  encoder_no_repeat_ngram_size: 4
+  length_penalty: 0.4
+  num_beams: 4
+pipeline_tag: summarization
+tags:
+- lay summaries
+- paper summaries
+- biology
+- medical
+library_name: transformers
 ---
 
 # long-t5-tglobal-base-sci-simplify
@@ -207,6 +207,25 @@ It achieves the following results on the evaluation set:
 
 - 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 procedure
 
 ### Training hyperparameters