readme: ---
  license: 
  - cc-by-nc-sa-4.0
  - apache-2.0
  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: "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: 4
    repetition_penalty: 1.21
    length_penalty: 1
    early_stopping: True
  ---
  
  
  # grammar-synthesis-large - beta
  
  A fine-tuned version of [google/t5-v1_1-large](https://huggingface.co/google/t5-v1_1-large) for grammar correction on an expanded version of the [JFLEG](https://paperswithcode.com/dataset/jfleg) dataset.
  
  usage in Python (after `pip install transformers`):
  
  ```python
  from transformers import pipeline
  
  corrector = pipeline(
                'text2text-generation',
                'pszemraj/grammar-synthesis-large',
                )
  raw_text = 'i can has cheezburger'
  results = corrector(raw_text)
  print(results)
  ```
  
  give it a spin in Colab at [this notebook](https://colab.research.google.com/gist/pszemraj/be3d1d060d1da14768af75c66429dc44/grammar-synthesis-large.ipynb)
  
  ## 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 :)
  
  ### Other checkpoints
  
  If trading a slight decrease in grammatical correction quality for faster inference speed makes sense for your use case, check out the **[base](https://huggingface.co/pszemraj/grammar-synthesis-base)** and **[small](https://huggingface.co/pszemraj/grammar-synthesis-small)** checkpoints fine-tuned from the relevant t5 checkpoints. 
  
  ## 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 t