Update README.md

README.md

@@ -39,18 +39,23 @@ parameters:
 
 # Grammar-Synthesis-Enhanced: FLAN-t5
 
- <a href="https://colab.research.google.com/gist/pszemraj/5dc89199a631a9c6cfd7e386011452a0/demo-flan-t5-large-grammar-synthesis.ipynb">
+<a href="https://colab.research.google.com/gist/pszemraj/5dc89199a631a9c6cfd7e386011452a0/demo-flan-t5-large-grammar-synthesis.ipynb">
   <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
 </a>
 
-A fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) for grammar correction on an expanded version of the [JFLEG](https://paperswithcode.com/dataset/jfleg) dataset and further fine-tuned using the [C4 200M](https://www.tensorflow.org/datasets/community_catalog/huggingface/c4) dataset. [Demo](https://huggingface.co/spaces/pszemraj/FLAN-grammar-correction) on HF spaces.
+This model is a fine-tuned version of [pszemraj/flan-t5-large-grammar-synthesis](https://huggingface.co/pszemraj/flan-t5-large-grammar-synthesis) using the C4 200M dataset for the NaraSpeak Bangkit 2024 ENTR-H130 application.
 
+## T5 Model Overview
 
-## Usage in Python 
+The T5 (Text-To-Text Transfer Transformer) model, introduced by Google Research, is a transformer-based model that treats every NLP task as a text-to-text problem. This unified approach allows T5 to excel at a variety of tasks, such as translation, summarization, and question answering, by converting inputs and outputs into text format.
 
-> There's a colab notebook that already has this basic version implemented (_click on the Open in Colab button_)
+### Transformer Architecture
 
-After `pip install transformers` run the following code:
+Transformers are a type of deep learning model designed for sequence-to-sequence tasks. They utilize a mechanism called "attention" to weigh the influence of different words in a sequence, allowing the model to focus on relevant parts of the input when generating each word in the output. This architecture is highly parallelizable and has proven effective in NLP tasks.
+
+## Usage in Python
+
+After `pip install transformers`, run the following code:
 
 ```python
 from transformers import pipeline