metadata
languages:
- en
license:
- cc-by-nc-sa-4.0
- apache-2.0
tags:
- grammar
- spelling
- punctuation
- error-correction
- grammar synthesis
- FLAN
datasets:
- jfleg
widget:
- text: There car broke down so their hitching a ride to they're class.
example_title: compound-1
- text: i can has cheezburger
example_title: cheezburger
- 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 2
- 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
- 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: flan-t5-xl
This model is a fine-tuned version of google/flan-t5-xl on an extended version of the JFLEG dataset.
Model description
The intent is to create a text2text language model that successfully performs "single-shot grammar correction" on a potentially grammatically incorrect text that could have many errors with the important qualifier that it does not semantically change text/information that IS grammatically correct..
Compare some of the more severe error examples on other grammar correction models to see the difference :)
Limitations
- Data set:
cc-by-nc-sa-4.0 - Model:
apache-2.0 - currently a work in progress! While probably useful for "single-shot grammar correction" in many cases, check the output for correctness, ok?.
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
Session One
- TODO: add this. It was a single epoch at higher LR
Session Two
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.02
- num_epochs: 2.0