metadata

license:
  - cc-by-nc-sa-4.0
  - apache-2.0
tags:
  - grammar
  - spelling
  - punctuation
  - error-correction
  - grammar synthesis
  - FLAN
datasets:
  - jfleg
languages:
  - en
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: simple 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
inference:
  parameters:
    max_length: 96
    min_length: 4
    num_beams: 2
    repetition_penalty: 1.15
    length_penalty: 1
    early_stopping: true
base_model: google/flan-t5-xl

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.

here is a custom class wrapper that makes using this with bitsandbytes easier
the API can be slow due to model size, try the notebook!

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 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