Update README.md

README.md

@@ -123,7 +123,7 @@ We finetuned the base model (flan-t5-large) on multiple relevant tasks with stan
 | Total                              | Challenge-proportional Mixing    | n/a                                        | 263,400         |
 
 
-- Multi-instruction pretuning: In the stage, we first created a task mixture using "challenge-proportional mixing" method. In a seperate pilot studie, for each task, we finetuned it on a base model and observed the number of samples when validation loss starts to rise. We mixed the samples of each task proportional to its optimal number of samples. A corpus is exhausted before upsampling if the number of total samples is smaller than its optimal number. We finetune with the task mixture (263,400 samples) with the aforementioned template.
+- Multi-instruction pretuning: In the stage, we first created a task mixture using "challenge-proportional mixing" method. In a separate pilot study, for each task, we finetuned it on a base model and observed the number of samples when validation loss starts to rise. We mixed the samples of each task proportional to its optimal number of samples. A corpus is exhausted before upsampling if the number of total samples is smaller than its optimal number. We finetune with the task mixture (263,400 samples) with the aforementioned template.
 
 - fine-tuning: In this stage, we continued finetuning the checkpoint solely with the Scientific Abstract-Significance corpus till optimal validation loss was observed.