---
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
---

## 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