---
license:
- cc-by-nc-sa-4.0
- apache-2.0
tags:
- grammar
- spelling
- punctuation
- error-correction
- grammar synthesis
datasets:
- jfleg
widget:
- text: There car broke down so their hitching a ride to they're class.
  example_title: compound-1
- 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: >-
    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
inference:
  parameters:
    max_length: 128
    min_length: 4
    num_beams: 4
    repetition_penalty: 1.21
    length_penalty: 1
    early_stopping: true
pipeline_tag: text2text-generation
language:
- en
---


# bart-base-grammar-synthesis

<a href="https://colab.research.google.com/gist/pszemraj/3e44e384dcd4614e1350d457bf9be8ad/bart-batch-grammar-check-correct-demo.ipynb">
  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>


This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an expanded version of the JFLEG dataset.

You can find other grammar-synthesis models by [searching for the grammar synthesis tag](https://huggingface.co/models?other=grammar%20synthesis)


## Intended uses & limitations

- robust grammar correction
- the model has a license of `cc-by-nc-sa-4.0` as it uses the JFLEG dataset + augments it for training

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 16
- 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: 3.0