languages:
- en
license:
- cc-by-nc-sa-4.0
- apache-2.0
tags:
- grammar
- spelling
- punctuation
- error-correction
- grammar synthesis
- FLAN
- C4
datasets:
- C4
widget:
- text: >-
Me go to the store yesterday and buy many thing. I saw a big dog but he no
bark at me. Then I walk home and eat my lunch, it was delicious sandwich.
After that, I watch TV and see a funny show about cat who can talk. I
laugh so hard I cry. Then I go to bed but I no can sleep because I too
excited about the cat show.
example_title: Long-Text
- text: >-
Me and my family go on a trip to the mountains last week. We drive for
many hours and finally reach our cabin. The cabin was cozy and warm, with
a fireplace and big windows. We spend our days hiking and exploring the
forest. At night, we sit by the fire and tell story. It was a wonderful
vacation.
example_title: Long-Text
- text: >-
so em if we have an now so with fito ringina know how to estimate the tren
given the ereafte mylite trend we can also em an estimate is nod s i again
tort watfettering an we have estimated the trend an called wot to be
called sthat of exty right now we can and look at wy this should not hare
a trend i becan we just remove the trend an and we can we now estimate
tesees ona effect of them exty
example_title: Transcribed Audio Example
- text: >-
My coworker said he used a financial planner to help choose his stocks so
he wouldn't loose money.
example_title: incorrect word choice
- text: >-
good so hve on an tadley i'm not able to make it to the exla session on
monday this week e which is why i am e recording pre recording an this
excelleision and so to day i want e to talk about two things and first of
all em i wont em wene give a summary er about ta ohow to remove trents in
these nalitives from time series
example_title: lowercased audio transcription output
parameters:
max_length: 128
min_length: 4
num_beams: 8
repetition_penalty: 1.21
length_penalty: 1
early_stopping: true
Grammar-Synthesis-Enhanced: FLAN-t5
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)