readme: ---
  license: cc-by-nc-sa-4.0
  tags:
  - grammar
  - spelling
  - punctuation
  - error-correction
  - grammar synthesis
  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: "Semo eaxmeslp of bda gmaramr ttah occru deu to nounprnooun ageremten errrso inlceud Anan adn Pat aer mairred he has bnee togethre fro 20 yaesr Anna and Pta aer plraul wheil he is sniurgla Teh sentecne suhold rdea Aann adn Pat are mraried tyhe heav"
    example_title: "descramble unintelligible text"
  - 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"
    
  parameters:
    max_length: 128
    min_length: 2
    num_beams: 8
    repetition_penalty: 1.5
    length_penalty: 0.95
    early_stopping: True
  ---
  
  
  # grammar-synthesis-base (beta)
  
  a fine-tuned version of [google/t5-base-lm-adapt](https://huggingface.co/google/t5-base-lm-adapt) for grammar correction on an expanded version of the [JFLEG](https://paperswithcode.com/dataset/jfleg) dataset. Check out a [demo notebook on Colab here](https://colab.research.google.com/gist/pszemraj/91abb08aa99a14d9fdc59e851e8aed66/demo-for-grammar-synthesis-base.ipynb).
  
  usage in Python (after `pip install transformers`):
  
  ```python
  from transformers import pipeline
  corrector = pipeline(
                'text2text-generation',
                'pszemraj/grammar-synthesis-base',
                )
  raw_text = 'i can has cheezburger'
  results = corrector(raw_text)
  print(results)
  ```
  
  ## Model description
  
  The intent is to create a text2text language model that successfully completes "single-shot grammar correction" on a potentially grammatically incorrect text **that could have a lot of mistakes** with the important qualifier of **it does not semantically change text/information that IS grammatically correct.**
  
  Compare some of the heavier-error examples on [other grammar correction models](https://huggingface.co/models?dataset=dataset:jfleg) to see the difference :)
  
  ## Limitations
  
  - dataset: `cc-by-nc-sa-4.0`
  - model: `apache-2.0`
  - this is **still a work-in-progress** and while probably useful for "single-shot grammar correction" in a lot of cases, **give the outputs a glance for correctness ok?**
  
  ## Use Cases
  
  Obviously, this section is quite general as there are many things one can use "general single-shot grammar correction" for. Some ideas or use cases:
  
  1. Correcting highly error-prone LM outputs. Some examples would be audio transcription (ASR) (this is literally some of the examples) or something like handwriting OCR. 
      - To be investigated further, depending on what model/system is used it _might_ be worth it to apply this after OCR on typed characters. 
  2. Correcting/infilling text generated by text generation models to be cohesive/remove obvious errors that break the conversation immersion. I use this on the outputs of [this OPT 2.7B chatbot-esque model of myself](https://huggingface.co/pszemraj/opt-peter-2.7B).
    > An example of this model running on CPU with beam search:
    
  ```
  original response:
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  synthesizing took 306.12 seconds
  Final response in 1294.857 s:
          I've heard it attributed to a bunch of different philosophical schools, including solipsism, pragmatism, ex