Update README.md

README.md

@@ -82,18 +82,21 @@ A fine-tuned version of [google/long-t5-tglobal-base](https://huggingface.co/goo
 
 - 20+ epochs of fine-tuning from the base model on V100/A100 GPUs
 - all training used 16384 token input / 1024 max output
-- early checkpoints of this model were trained on a "smaller" subsection of the dataset as it was filtered for summaries of **1024 characters**. This was subsequently caught and adjusted to **1024 tokens**and then trained further for at least five epochs. 
+
 
 ## Intended uses & limitations
 
 - At the time of writing, the model is not _fully converged_ despite training for 20+ epochs. This checkpoint is serviceable enough (see examples).
-- I plan to update this page with newer checkpoints and post some metrics over time.
-- Compare performance to [LED-base](https://huggingface.co/pszemraj/led-base-book-summary) trained on the same dataset (API gen parameters are the same).
+  - I plan to update this page with newer checkpoints and post some metrics over time.
+  - Compare performance to [LED-base](https://huggingface.co/pszemraj/led-base-book-summary) trained on the same dataset (API gen parameters are the same).
+- while this model seems to improve upon factual consistency, **do not take summaries to be foolproof and check things that seem odd**.
 
 ## Training and evaluation data
 
 `kmfoda/booksum` dataset. Summaries longer than 1024 LongT5 tokens were filtered out with the intent of preventing the model from learning to generate "partial" summaries.
 
+> - early checkpoints of this model were trained on a "smaller" subsection of the dataset as it was filtered for summaries of **1024 characters**. This was subsequently caught and adjusted to **1024 tokens** and then trained further for at least five epochs. 
+
 ## Training procedure
 
 ### Training hyperparameters