Update README.md

README.md

@@ -19,7 +19,7 @@ metrics:
 
 # Multi-purpose Summarizer (Fine-tuned 3B google/flan-t5-xl on several Summarization datasets)
 
- <a href="https://colab.research.google.com/gist/pszemraj/5dc89199a631a9c6cfd7e386011452a0/demo-flan-t5-large-grammar-synthesis.ipynb">
+ <a href="https://colab.research.google.com/drive/1EYfnIoG-r5lL2-3oiO_YdYEVKB0pAa9h">
   <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
 </a>
 
@@ -67,18 +67,11 @@ results = summarizer(
     )
 ```
 
-**For Batch Inference:** see [this discussion thread](https://huggingface.co/pszemraj/flan-t5-large-grammar-synthesis/discussions/1) for details, but essentially the dataset consists of several sentences at a time, and so I'd recommend running inference **in the same fashion:** batches of 64-96 tokens ish (or, 2-3 sentences split with regex) 
-
-- it is also helpful to **first** check whether or not a given sentence needs grammar correction before using the text2text model. You can do this with BERT-type models fine-tuned on CoLA like `textattack/roberta-base-CoLA`
-- I made a notebook demonstrating batch inference [here](https://colab.research.google.com/gist/pszemraj/6e961b08970f98479511bb1e17cdb4f0/batch-grammar-check-correct-demo.ipynb)
-
-
-
 ---
 
 ## Training procedure
 
-- Training was done in BF16, deepspeed stage 2 for 6 epochs with ROUGE2 monitored on the validation set.
+- Training was done in BF16, deepspeed stage 2 for 6 epochs with ROUGE-2 monitored on the validation set.
 
 ## Hardware
 - GPU count	8 NVIDIA A100-SXM4-40GB