metadata
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 (context)
- 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
- text: Frustrated, the chairs took me forever to set up.
example_title: dangling modifier
- text: I would like a peice of pie.
example_title: miss-spelling
- text: >-
Which part of Zurich was you going to go hiking in when we were there for
the first time together? ! ?
example_title: chatbot on Zurich
- text: >-
Most of the course is about semantic or content of language but there are
also interesting topics to be learned from the servicefeatures except
statistics in characters in documents. At this point, Elvthos introduces
himself as his native English speaker and goes on to say that if you
continue to work on social scnce,
example_title: social science ASR summary output
- text: >-
they are somewhat nearby right yes please i'm not sure how the innish is
tepen thut mayyouselect one that istatte lo variants in their property e
ere interested and anyone basical e may be applyind reaching the browing
approach were
example_title: medical course audio transcription
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
A fine-tuned version of google/flan-t5-large for grammar correction on an expanded version of the JFLEG dataset and further fine-tuned using the C4 200M dataset. Demo on HF spaces.
Example
Compare vs. the original grammar-synthesis-large.
Usage in Python
There's a colab notebook that already has this basic version implemented (click on the Open in Colab button)
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)