

# 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)

## Basic Usage Example

### Installation

First, make sure you have the `transformers` package installed. You can install it using pip:

```
pip install -U transformers
```

### Usage

```python
from transformers import pipeline

# Initialize the text-generation pipeline for text correction
corrector = pipeline("text2text-generation", "pszemraj/bart-base-grammar-synthesis")

# Example text to correct
raw_text = "The toweris 324 met (1,063 ft) tall, about height as .An 81-storey building, and biggest longest structure paris. Is square, measuring 125 metres (410 ft) on each side. During its constructiothe eiffel tower surpassed the washington monument to become the tallest man-made structure in the world, a title it held for 41 yearsuntilthe chryslerbuilding in new york city was finished in 1930. It was the first structure to goat a height of 300 metres. Due 2 the addition ofa brdcasting aerial at the t0pp of the twr in 1957, it now taller than  chrysler building 5.2 metres (17 ft). Exxxcluding transmitters,  eiffel tower is  2ndd tallest ree-standing structure in france after millau viaduct."

# Correct the text using the text-generation pipeline
corrected_text = corrector(raw_text)[0]["generated_text"]

# Print the corrected text
print(corrected_text)
```

This example demonstrates how to use the text-generation pipeline to correct the grammar in a given text. The `corrector` pipeline is initialized with the "pszemraj/bart-base-grammar-synthesis" model, which is designed for grammar correction. The `corrector` pipeline takes the raw text as input and returns the corrected text. Make sure to install the required dependencies and models before running the code.


## 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

{"language": ["en"], "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": false, "pipeline_tag": "text2text-generation"}
transformers pytorch onnx safetensors bart text2text-generation grammar spelling punctuation error-correction grammar synthesis en dataset:jfleg license:cc-by-nc-sa-4.0 license:apache-2.0 autotrain_compatible region:us
TAGS
#transformers #pytorch #onnx #safetensors #bart #text2text-generation #grammar #spelling #punctuation #error-correction #grammar synthesis #en #dataset-jfleg #license-cc-by-nc-sa-4.0 #license-apache-2.0 #autotrain_compatible #region-us 



# bart-base-grammar-synthesis

<a href="URL
  <img src="URL alt="Open In Colab"/>
</a>


This model is a fine-tuned version of 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

## Basic Usage Example

### Installation

First, make sure you have the 'transformers' package installed. You can install it using pip:



### Usage



This example demonstrates how to use the text-generation pipeline to correct the grammar in a given text. The 'corrector' pipeline is initialized with the "pszemraj/bart-base-grammar-synthesis" model, which is designed for grammar correction. The 'corrector' pipeline takes the raw text as input and returns the corrected text. Make sure to install the required dependencies and models before running the code.


## 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

# bart-base-grammar-synthesis

<a href="URL
  <img src="URL alt="Open In Colab"/>
</a>


This model is a fine-tuned version of 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 ## Basic Usage Example ### Installation

First, make sure you have the 'transformers' package installed. You can install it using pip: ### Usage



This example demonstrates how to use the text-generation pipeline to correct the grammar in a given text. The 'corrector' pipeline is initialized with the "pszemraj/bart-base-grammar-synthesis" model, which is designed for grammar correction. The 'corrector' pipeline takes the raw text as input and returns the corrected text. Make sure to install the required dependencies and models before running the code. ## 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
TAGS
#transformers #pytorch #onnx #safetensors #bart #text2text-generation #grammar #spelling #punctuation #error-correction #grammar synthesis #en #dataset-jfleg #license-cc-by-nc-sa-4.0 #license-apache-2.0 #autotrain_compatible #region-us 
 # bart-base-grammar-synthesis

<a href="URL
  <img src="URL alt="Open In Colab"/>
</a>


This model is a fine-tuned version of 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 ## Basic Usage Example ### Installation

First, make sure you have the 'transformers' package installed. You can install it using pip: ### Usage



This example demonstrates how to use the text-generation pipeline to correct the grammar in a given text. The 'corrector' pipeline is initialized with the "pszemraj/bart-base-grammar-synthesis" model, which is designed for grammar correction. The 'corrector' pipeline takes the raw text as input and returns the corrected text. Make sure to install the required dependencies and models before running the code. ## 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