add usage

README.md

@@ -204,6 +204,7 @@ parameters:
 
 An experiment investigating transfer learning capabilities by fine-tuning models on different datasets starting from the `booksum` checkpoint.
 
+
 ## Model Details
 
 This model is a fine-tuned version of [pszemraj/long-t5-tglobal-base-16384-book-summary](https://huggingface.co/pszemraj/long-t5-tglobal-base-16384-book-summary) on the `pszemraj/scientific_lay_summarisation-elife-norm` dataset for two epochs.
@@ -216,6 +217,27 @@ It achieves the following results on the evaluation set:
 - Rougelsum: 30.6965
 - Gen Len: 294.0249
 
+
+## 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
+
+model_name = "pszemraj/long-t5-tglobal-base-16384-booksci-summary-v1"
+summarizer = Summarizer(model_name) # GPU auto-detected
+text = "put the text you don't want to read here"
+summary = summarizer.summarize_string(text)
+print(summary)
+```
+
+
 ## Intended uses & limitations
 
 - This is an initial experiment