readme: ---
  license: cc-by-nc-sa-4.0
  tags:
  - grammar
  - spelling
  - punctuation
  - error-correction
  datasets:
  - jfleg
  widget:
  - text: "i can has cheezburger"
    example_title: "cheezburger"
  - text: "There car broke down so their hitching a ride to they're class."
    example_title: "compound-1"
  - 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"
  
  parameters:
    max_length: 128
    min_length: 4
    num_beams: 4
    repetition_penalty: 1.21
    length_penalty: 1
    early_stopping: True
  ---
  
  > A more recent version can be found [here](https://huggingface.co/pszemraj/grammar-synthesis-large). Training smaller and/or comparably sized models is a WIP.
  
  # t5-v1_1-base-ft-jflAUG
  
  **GOAL:** a more robust and generalized grammar and spelling correction model that corrects everything in a single shot. It should have a minimal impact on the semantics of correct sentences (i.e. it does not change things that do not need to be changed).
  
  - this model _(at least from preliminary testing)_ can handle large amounts of errors in the source text (i.e. from audio transcription) and still produce cohesive results. 
  - a fine-tuned version of [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) on an expanded version of the [JFLEG dataset](https://aclanthology.org/E17-2037/).
  
  ## Model description
  
  - this is a WIP. This fine-tuned model is v1.
  - long term: a generalized grammar and spelling correction model that can handle lots of things at the same time.
  - currently, it seems to be more of a "gibberish to mostly correct English" translator
  
  ## Intended uses & limitations
  
  - try some tests with the [examples here](https://www.engvid.com/english-resource/50-common-grammar-mistakes-in-english/)
  - thus far, some limitations are: sentence fragments are not autocorrected (at least, if entered individually), some more complicated pronoun/they/he/her etc. agreement is not always fixed.
  
  ## Training and evaluation data
  
  - trained as text-to-text
  - JFLEG dataset + additional selected and/or generated grammar corrections
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 6e-05
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - distributed_type: multi-GPU
  - gradient_accumulation_steps: 8
  - 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: 0.05
  - num_epochs: 5
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.10.0+cu111
  - Datasets 2.0.0
  - Tokenizers 0.11.6
  