---
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
---


# grammar-synthesis-small - beta

This model is a fine-tuned version of [pszemraj/grammar-synthesis-small-WIP](https://huggingface.co/pszemraj/grammar-synthesis-small-WIP) on an unknown dataset.

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0004
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 32
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 4

### Training results



### Framework versions

- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1