---
license:
- cc-by-nc-sa-4.0
- apache-2.0
tags:
- grammar
- spelling
- punctuation
- error-correction
- grammar synthesis
- FLAN
datasets:
- jfleg
languages:
- en
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: simple 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
- example_title: medical course audio transcription
inference:
  parameters:
    max_length: 96
    min_length: 4
    num_beams: 2
    repetition_penalty: 1.15
    length_penalty: 1
    early_stopping: true
base_model: google/flan-t5-xl
---

# grammar-synthesis: flan-t5-xl

<a href="https://colab.research.google.com/gist/pszemraj/43fc6a5c5acd94a3d064384dd1f3654c/demo-flan-t5-xl-grammar-synthesis.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 [google/flan-t5-xl](https://huggingface.co/google/flan-t5-xl) on an extended version of the `JFLEG` dataset.

- [here is a custom class wrapper](https://gist.github.com/pszemraj/14f7b13bd2d953176db2371e5d320915) that makes using this with `bitsandbytes` easier
- the API can be slow due to model size, try [the notebook](https://colab.research.google.com/gist/pszemraj/43fc6a5c5acd94a3d064384dd1f3654c/demo-flan-t5-xl-grammar-synthesis.ipynb)!

<br>
<img src="https://i.imgur.com/5QGGF0Z.png" alt="ex">
<br>


## Model description

The intent is to create a text2text language model that successfully performs "single-shot grammar correction" on a potentially grammatically incorrect text **that could have many errors** with the important qualifier that **it does not semantically change text/information that IS grammatically correct.**.

Compare some of the more severe error examples on [other grammar correction models](https://huggingface.co/models?dataset=dataset:jfleg) to see the difference :)

## Limitations

- Data set: `cc-by-nc-sa-4.0`
- Model: `apache-2.0`
- currently **a work in progress**! While probably useful for "single-shot grammar correction" in many cases, **check the output for correctness, ok?**.


## Training procedure

### Training hyperparameters


#### Session One

- TODO: add this. It was a single epoch at higher LR

#### Session Two

The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 16
- 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: 0.02
- num_epochs: 2.0