---
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: "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: "I would like a peice of pie."
  example_title: "miss-spelling"
- 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: "There car broke down so their hitching a ride to they're class."
  example_title: "compound-1"
- 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
---


# 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`):

```
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/9b810e38a4d3bc766834df921818d782/scratchpad.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 :)

## 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).
  > TODO add an example
3. Somewhat related to #2 above, fixing/correcting so-called [tortured-phrases](https://arxiv.org/abs/2107.06751) that are dead giveaways text was generated by a language model. _Note that _SOME_ of these are not fixed, especially as they venture into domain-specific terminology (i.e. irregular timberland instead of Random Forest)._


## Training and evaluation data

More information needed 😉

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 8e-05
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 32
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.02
- num_epochs: 1

### Framework versions

- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1