NaraSpeak_GEC_V1

Grammar-Synthesis-Enhanced: FLAN-t5

<a href="https://colab.research.google.com/gist/Aelzi/25fee0b38c4687a2e9821d87980bbb09/demo-flan-t5-large-grammar-synthesis.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a>

This model is a fine-tuned version of pszemraj/flan-t5-large-grammar-synthesis using the C4 200M dataset for the NaraSpeak Bangkit 2024 ENTR-H130 application.

T5 Model Overview

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.

Transformer Architecture

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:

from transformers import pipeline

corrector = pipeline(
              'text2text-generation',
              'farelzii/GEC_Test_v1',
              )
raw_text = 'i can has cheezburger'
results = corrector(raw_text)
print(results)