Update README.md

README.md

@@ -206,20 +206,6 @@ Exploring how well long-document models trained on "lay summaries" of scientific
 
 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.
 
-It achieves the following results on the evaluation set:
-- Loss: 1.9990
-- Rouge1: 38.5587
-- Rouge2: 9.7336
-- Rougel: 21.1974
-- Rougelsum: 35.9333
-- Gen Len: 392.7095
-
-
-## Intended uses & limitations
-
-- 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:
@@ -239,12 +225,27 @@ summary = summarizer.summarize_string(text)
 print(summary)
 ```
 
+## Intended uses & limitations
+
+- Ability to generalize outside of the dataset domain (pubmed/bioscience type papers) has to be evaluated.
+
 ## Training and evaluation data
 
 The `elife` subset of the :lay summaries dataset. Refer to `pszemraj/scientific_lay_summarisation-elife-norm`
 
 ## Training procedure
 
+
+### Eval results
+
+It achieves the following results on the evaluation set:
+- Loss: 1.9990
+- Rouge1: 38.5587
+- Rouge2: 9.7336
+- Rougel: 21.1974
+- Rougelsum: 35.9333
+- Gen Len: 392.7095
+
 ### Training hyperparameters
 
 The following hyperparameters were used during training: