---
license: apache-2.0
tags:
- grammar
- spelling
- punctuation
- error-correction

widget:
- text: "Anna and Mike is going skiing"
  example_title: "skiing"
- 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"

inference:
  parameters:
    no_repeat_ngram_size: 4
    max_length: 64
    min_length: 4
    num_beams: 4
    repetition_penalty: 1.51
    length_penalty: 1
    early_stopping: True
---


# t5-v1_1-base-ft-jflAUG

> **GOAL:** a more robust and generalized grammar and spelling correction model with 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