README.md

---
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
---
---
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: 2
  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