metadata

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
language:
  - en

bart-base-grammar-synthesis

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:

pip install -U transformers

Usage

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